From 91c6ec965bf18666be06afc8812b34b686a6d795 Mon Sep 17 00:00:00 2001 From: jung-geun Date: Mon, 29 May 2023 04:01:48 +0900 Subject: [PATCH] =?UTF-8?q?23-05-29=20EBPSO=20=EC=95=8C=EA=B3=A0=EB=A6=AC?= =?UTF-8?q?=EC=A6=98=20=EA=B5=AC=ED=98=84=20-=20=EC=84=A0=ED=83=9D?= =?UTF-8?q?=EC=A7=80=EB=A1=9C=20=EC=B6=94=EA=B0=80=20random=20=EC=9C=BC?= =?UTF-8?q?=EB=A1=9C=20=EB=B6=84=EC=82=B0=EC=8B=9C=ED=82=A4=EB=8A=94=20?= =?UTF-8?q?=EB=B0=A9=EB=B2=95=20=EA=B5=AC=ED=98=84=20-=20=EC=84=A0?= =?UTF-8?q?=ED=83=9D=EC=A7=80=EB=A1=9C=20=EC=B6=94=EA=B0=80=20iris=20?= =?UTF-8?q?=EA=B8=B0=EC=A4=80=2098=ED=8D=BC=EC=84=BC=ED=8A=B8=EB=A1=9C=20?= =?UTF-8?q?=EB=82=98=EC=98=A4=EB=82=98=20=EC=A0=95=ED=99=95=ED=95=9C=20?= =?UTF-8?q?=EA=B2=B0=EA=B3=BC=EB=A5=BC=20=EC=A7=80=EC=BC=9C=EB=B4=90?= =?UTF-8?q?=EC=95=BC=20=ED=95=A0=EA=B2=83=EC=9C=BC=EB=A1=9C=20=EB=B3=B4?= =?UTF-8?q?=EC=9E=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 2 +- example.py | 103 ++ iris.py | 48 + pso_bp.py => metacode/pso_bp.py | 0 pso_meta.py => metacode/pso_meta.py | 0 metacode/pso_tf.py | 268 +++++ pso_tf.py => metacode/pso_tf_bak.py | 155 +-- mnist.ipynb | 1031 ------------------ mnist.py | 124 +-- plt.ipynb | 126 +++ pso/__init__.py | 5 + pso/optimizer.py | 303 ++++++ pso/particle.py | 124 +++ pso_tuning.py | 155 --- psokeras/__init__.py | 14 + psokeras/optimizer.py | 67 ++ psokeras/particle.py | 66 ++ psokeras/util.py | 31 + psokeras/version.py | 1 + pyswarms/example.ipynb | 484 +++++++++ pyswarms/pso.gif | Bin 0 -> 4218 bytes pyswarms/pyswarm.py | 93 ++ pyswarms/report.log | 11 + readme.md | 5 + readme.png | Bin 228442 -> 0 bytes test.ipynb | 306 ++++++ xor.ipynb | 1503 ++++++++++++++++++++++----- 27 files changed, 3378 insertions(+), 1647 deletions(-) create mode 100755 example.py create mode 100644 iris.py rename pso_bp.py => metacode/pso_bp.py (100%) rename pso_meta.py => metacode/pso_meta.py (100%) create mode 100644 metacode/pso_tf.py rename pso_tf.py => metacode/pso_tf_bak.py (55%) delete mode 100644 mnist.ipynb create mode 100644 plt.ipynb create mode 100644 pso/__init__.py create mode 100644 pso/optimizer.py create mode 100644 pso/particle.py delete mode 100644 pso_tuning.py create mode 100755 psokeras/__init__.py create mode 100755 psokeras/optimizer.py create mode 100755 psokeras/particle.py create mode 100755 psokeras/util.py create mode 100755 psokeras/version.py create mode 100644 pyswarms/example.ipynb create mode 100644 pyswarms/pso.gif create mode 100644 pyswarms/pyswarm.py create mode 100644 pyswarms/report.log delete mode 100644 readme.png create mode 100644 test.ipynb diff --git a/.gitignore b/.gitignore index 5ca7509..786aa82 100644 --- a/.gitignore +++ b/.gitignore @@ -2,4 +2,4 @@ __pycache__/ .ipynb_checkpoints/ *.pdf -model/ \ No newline at end of file +result/ \ No newline at end of file diff --git a/example.py b/example.py new file mode 100755 index 0000000..98d469f --- /dev/null +++ b/example.py @@ -0,0 +1,103 @@ +""" +example.py + +Demonstrates usage of PSOkeras module by training dense Keras model for classifying Iris data set. Also compares +results with a number of independent runs of standard Backpropagation algorithm (Adam) equal to the particle count. + +@author Mike Holcomb (mjh170630@utdallas.edu) +""" + +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense + +from psokeras import Optimizer + +N = 50 # number of particles +STEPS = 500 # number of steps +LOSS = 'mse' # Loss function +BATCH_SIZE = 32 # Size of batches to train on + + +def build_model(loss): + """ + Builds test Keras model for predicting Iris classifications + + :param loss (str): Type of loss - must be one of Keras accepted keras losses + :return: Keras dense model of predefined structure + """ + model = Sequential() + model.add(Dense(4, activation='sigmoid', input_dim=4, use_bias=True)) + model.add(Dense(4, activation='sigmoid', use_bias=True)) + model.add(Dense(3, activation='softmax', use_bias=True)) + + model.compile(loss=loss, + optimizer='adam') + + return model + + +def vanilla_backpropagation(x_train, y_train): + """ + Runs N number of backpropagation model training simulations + :param x_train: x values to train on + :param y_train: target labels to train with + :return: best model run as measured by LOSS + """ + best_model = None + best_score = 100.0 + + for i in range(N): + model_s = build_model(LOSS) + model_s.fit(x_train, y_train, + epochs=STEPS, + batch_size=BATCH_SIZE, + verbose=0) + train_score = model_s.evaluate(x_train, y_train, batch_size=BATCH_SIZE, verbose=0) + if train_score < best_score: + best_model = model_s + best_score = train_score + return best_model + + +if __name__ == "__main__": + # Section I: Build the data set + iris = load_iris() + x_train, x_test, y_train, y_test = train_test_split(iris.data, + keras.utils.to_categorical(iris.target, num_classes=None), + test_size=0.5, + random_state=0, + stratify=iris.target) + + # Section II: First run the backpropagation simulation + model_s = vanilla_backpropagation(x_train=x_train, y_train=y_train) + + b_train_score = model_s.evaluate(x_train, y_train, batch_size=BATCH_SIZE, verbose=0) + b_test_score = model_s.evaluate(x_test, y_test, batch_size=BATCH_SIZE, verbose=0) + print("Backprop -- train: {:.4f} test: {:.4f}".format(b_train_score, b_test_score)) + + # Section III: Then run the particle swarm optimization + # First build model to train on (primarily used for structure, also included in swarm) + model_p = build_model(LOSS) + + # Instantiate optimizer with model, loss function, and hyperparameters + pso = Optimizer(model=model_p, + loss=LOSS, + n=N, # Number of particles + acceleration=1.0, # Contribution of recursive particle velocity (acceleration) + local_rate=0.6, # Contribution of locally best weights to new velocity + global_rate=0.4 # Contribution of globally best weights to new velocity + ) + + # Train model on provided data + pso.fit(x_train, y_train, steps=STEPS, batch_size=BATCH_SIZE) + + # Get a copy of the model with the globally best weights + model_p = pso.get_best_model() + + p_train_score = model_p.evaluate(x_train, y_train, batch_size=BATCH_SIZE, verbose=0) + p_test_score = model_p.evaluate(x_test, y_test, batch_size=BATCH_SIZE, verbose=0) + print("PSO -- train: {:.4f} test: {:.4f}".format(p_train_score, p_test_score)) diff --git a/iris.py b/iris.py new file mode 100644 index 0000000..8340c62 --- /dev/null +++ b/iris.py @@ -0,0 +1,48 @@ +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + +import tensorflow as tf +tf.random.set_seed(777) # for reproducibility + +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split + +from tensorflow import keras +from tensorflow.keras.models import Sequential +from tensorflow.keras import layers + +from pso import Optimizer + +import gc + +def make_model(): + model = Sequential() + model.add(layers.Dense(10, activation='relu', input_shape=(4,))) + model.add(layers.Dense(10, activation='relu')) + model.add(layers.Dense(3, activation='softmax')) + + return model + +def load_data(): + iris = load_iris() + x = iris.data + y = iris.target + + y = keras.utils.to_categorical(y, 3) + + x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, shuffle=True, stratify=y) + + return x_train, x_test, y_train, y_test + +model = make_model() +x_train, x_test, y_train, y_test = load_data() + +loss = 'categorical_crossentropy' + +pso_iris = Optimizer(model, loss=loss, n_particles=50, c0=0.4, c1=0.8, w_min=0.7, w_max=1.3) +weight, score = pso_iris.fit( + x_train, y_train, epochs=500, save=True, save_path="./result/iris", renewal="acc", empirical_balance=True, Dispersion=True, check_point=50) +pso_iris.model_save("./result/iris") +pso_iris.save_info("./result/iris/") + +gc.collect() diff --git a/pso_bp.py b/metacode/pso_bp.py similarity index 100% rename from pso_bp.py rename to metacode/pso_bp.py diff --git a/pso_meta.py b/metacode/pso_meta.py similarity index 100% rename from pso_meta.py rename to metacode/pso_meta.py diff --git a/metacode/pso_tf.py b/metacode/pso_tf.py new file mode 100644 index 0000000..c7f2bfa --- /dev/null +++ b/metacode/pso_tf.py @@ -0,0 +1,268 @@ +import os +import numpy as np +from tqdm import tqdm +from matplotlib import pyplot as plt +import pandas as pd + +import tensorflow as tf +from tensorflow import keras + +import datetime +import gc +import cupy as cp + + + +class PSO(object): + """ + Class implementing PSO algorithm + """ + + def __init__(self, model: keras.models, loss_method=keras.losses.MeanSquaredError(), n_particles: int = 5): + """ + Initialize the key variables. + + Args: + model : 학습할 모델 객체 (Sequential) + loss_method : 손실 함수 + n_particles(int) : 파티클의 개수 + """ + self.model = model # 모델 + self.n_particles = n_particles # 파티클의 개수 + self.loss_method = loss_method # 손실 함수 + model_structure = self.model.to_json() # 모델의 구조 정보 + self.init_weights = self.model.get_weights() # 검색할 차원 + self.particle_depth = len(self.model.get_weights()) # 검색할 차원의 깊이 + self.particles_weights = [None] * n_particles # 파티클의 위치 + for _ in tqdm(range(self.n_particles), desc="init particles position"): + m = keras.models.model_from_json(model_structure) + m.compile(loss=self.loss_method, + optimizer="adam", metrics=["accuracy"]) + self.particles_weights[_] = m.get_weights() + + # 입력받은 파티클의 개수 * 검색할 차원의 크기 만큼의 균등한 위치를 생성 + self.velocities = [ + [0 for i in range(self.particle_depth)] for n in range(n_particles)] + for i in tqdm(range(n_particles), desc="init velocities"): + + self.init_weights = self.model.get_weights() + w_,s_,l_ = self._encode(self.init_weights) + w_ = np.random.rand(len(w_)) / 5 - 0.10 + self.velocities[i] = self._decode(w_,s_,l_) + # for index, layer in enumerate(self.init_weights): + # self.velocities[i][index] = np.random.rand( + # *layer.shape) / 5 - 0.10 + + + # 입력받은 파티클의 개수 * 검색할 차원의 크기 만큼의 속도를 무작위로 초기화 + # 최대 사이즈로 전역 최적갑 저장 - global best + self.p_best = self.particles_weights # 각 파티클의 최적값(최적의 가중치) + self.g_best=self.model.get_weights() # 전역 최적값(최적의 가중치) | 초기값은 모델의 가중치 + + # 각 파티클의 최적값의 점수 + self.p_best_score = [0 for i in range(n_particles)] + + # 전역 최적값의 점수(초기화 - 0) + self.g_best_score = 0 + + def __del__(self): + del self.model + del self.n_particles + del self.loss_method + del self.init_weights + del self.particles_weights + del self.velocities + del self.p_best + del self.g_best + del self.p_best_score + del self.g_best_score + + def _encode(self,weights: list): + # w_gpu = cp.array([]) + w_gpu = np.array([]) + lenght = [] + shape = [] + for layer in weights: + shape.append(layer.shape) + w_ = layer.reshape(-1) + lenght.append(len(w_)) + w_gpu = np.append(w_gpu, w_) + # w_gpu = cp.append(w_gpu, w_) + + return w_gpu, shape, lenght + + def _decode(self,weight, shape, lenght): + weights = [] + start = 0 + for i in range(len(shape)): + end = start + lenght[i] + # print(f"{start} ~ {end}") + # print(f"{shape[i]}") + w_ = weight[start:end] + w_ = np.reshape(w_, shape[i]) + # w_ = w_.reshape(shape[i]) + weights.append(w_) + start = end + + return weights + + def _update_weights(self, weights, v): + """ + Update particle position + + Args: + weights (array-like) : 파티클의 현재 가중치 + v (array-like) : 가중치의 속도 + + Returns: + (array-like) : 파티클의 새로운 가중치(위치) + """ + # w = np.array(w) # 각 파티클의 위치 + # v = np.array(v) # 각 파티클의 속도(방향과 속력을 가짐) + # new_weights = [0 for i in range(len(weights))] + # print(f"weights : {weights}") + encode_w, w_sh, w_len = self._encode(weights = weights) + encode_v, _, _ = self._encode(weights = v) + new_w = encode_w + encode_v + new_weights = self._decode(new_w, w_sh, w_len) + + # for i in range(len(weights)): + # new_weights[i] = tf.add(weights[i], v[i]) + # new_w = tf.add(w, v) # 각 파티클을 랜덤한 속도만큼 진행 + return new_weights # 진행한 파티클들의 위치를 반환 + + def _update_velocity(self, weights, v, p_best, c0=0.5, c1=1.5, w=0.75): + """ + Update particle velocity + + Args: + weights (array-like) : 파티클의 현재 가중치 + v (array-like) : 속도 + p_best(array-like) : 각 파티클의 최적의 위치 (최적의 가중치) + c0 (float) : 인지 스케일링 상수 (가중치의 중요도 - 지역) - 지역 관성 + c1 (float) : 사회 스케일링 상수 (가중치의 중요도 - 전역) - 전역 관성 + w (float) : 관성 상수 (현재 속도의 중요도) + + Returns: + (array-like) : 각 파티클의 새로운 속도 + """ + # x = np.array(x) + # v = np.array(v) + # assert np.shape(weights) == np.shape(v), "Position and velocity must have same shape." + # 두 데이터의 shape 이 같지 않으면 오류 출력 + # 0에서 1사이의 숫자를 랜덤 생성 + r0 = np.random.rand() + r1 = np.random.rand() + # p_best = np.array(p_best) + # g_best = np.array(g_best) + + # 가중치(상수)*속도 + \ + # 스케일링 상수*랜덤 가중치*(나의 최적값 - 처음 위치) + \ + # 전역 스케일링 상수*랜덤 가중치*(전체 최적값 - 처음 위치) + + encode_w, w_sh, w_len = self._encode(weights = weights) + encode_v, _, _ = self._encode(weights = v) + encode_p, _, _ = self._encode(weights = p_best) + encode_g, _, _ = self._encode(weights = self.g_best) + + new_v = encode_w * encode_v + c0*r0*(encode_p - encode_w) + c1*r1*(encode_g - encode_w) + new_velocity = self._decode(new_v, w_sh, w_len) + # new_velocity = [None] * len(weights) + # for i, layer in enumerate(weights): + + # new_v = w*v[i] + # new_v = new_v + c0*r0*(p_best[i] - layer) + # new_v = new_v + c1*r1*(self.g_best[i] - layer) + # new_velocity[i] = new_v + + # new_v = w*v + c0*r0*(p_best - weights) + c1*r1*(g_best - weights) + return new_velocity + + def _get_score(self, x, y): + """ + Compute the score of the current position of the particles. + + Args: + x (array-like): The current position of the particles + y (array-like): The current position of the particles + Returns: + (array-like) : 추론에 대한 점수 + """ + score = self.model.evaluate(x, y, verbose=0) + + return score + + def optimize(self, x_, y_, maxiter=10, c0=0.5, c1=1.5, w=0.75, save=False, save_path="./result/history"): + """ + Run the PSO optimization process utill the stoping critera is met. + Cas for minization. The aim is to minimize the cost function + + Args: + maxiter (int): the maximum number of iterations before stopping the optimization + 파티클의 최종 위치를 위한 반복 횟수 + Returns: + The best solution found (array-like) + """ + if save: + os.makedirs(save_path, exist_ok=True) + day = datetime.datetime.now().strftime('%m-%d-%H-%M') + + for _ in range(maxiter): + + for i in tqdm(range(self.n_particles), desc=f"Iter {_}/{maxiter} ", ascii=True): + weights = self.particles_weights[i] # 각 파티클 추출 + v = self.velocities[i] # 각 파티클의 다음 속도 추출 + p_best = self.p_best[i] # 결과치 저장할 변수 지정 + # 2. 속도 계산 + self.velocities[i] = self._update_velocity( + weights, v, p_best, c0, c1, w) + # 다음에 움직일 속도 = 최초 위치, 현재 속도, 현재 위치, 최종 위치 + # 3. 위치 업데이트 + self.particles_weights[i] = self._update_weights(weights, v) + # 현재 위치 = 이전 위치 + 현재 속도 + # 내 현재 위치가 내 위치의 최소치보다 작으면 갱신 + self.model.set_weights(self.particles_weights[i]) + # self.particles_weights[i] = self.model.get_weights() + # 4. 평가 + self.model.compile(loss=self.loss_method, + optimizer='sgd', metrics=['accuracy']) + score = self._get_score(x_, y_) + + if score[1] > self.p_best_score[i]: + self.p_best_score[i] = score[1] + self.p_best[i] = self.particles_weights[i] + if score[1] > self.g_best_score: + self.g_best_score = score[1] + self.g_best = self.particles_weights[i] + + if save: + with open(f"{save_path}/{day}_{self.n_particles}_{maxiter}_{c0}_{c1}_{w}.csv",'a')as f: + f.write(f"{score[0]}, {score[1]}") + if i != self.n_particles - 1: + f.write(",") + + if save: + with open(f"{save_path}/{day}_{self.n_particles}_{maxiter}_{c0}_{c1}_{w}.csv",'a')as f: + f.write("\n") + print( + f"loss avg : {score[0]/self.n_particles} | acc avg : {score[1]/self.n_particles} | best score : {self.g_best_score}") + gc.collect() + + # 전체 최소 위치, 전체 최소 벡터 + return self.g_best, self._get_score(x_, y_) + + """ + Returns: + 최종 가중치 + """ + + def best_weights(self): + return self.g_best + + """ + Returns: + 최종 가중치의 스코어 + """ + + def best_score(self): + return self.g_best_score \ No newline at end of file diff --git a/pso_tf.py b/metacode/pso_tf_bak.py similarity index 55% rename from pso_tf.py rename to metacode/pso_tf_bak.py index 1093a19..5dafc83 100644 --- a/pso_tf.py +++ b/metacode/pso_tf_bak.py @@ -1,7 +1,10 @@ +import os import numpy as np +from tqdm import tqdm +from matplotlib import pyplot as plt + import tensorflow as tf from tensorflow import keras -from tqdm import tqdm class PSO(object): @@ -9,77 +12,49 @@ class PSO(object): Class implementing PSO algorithm """ - def __init__(self, model: keras.models, loss_method=keras.losses.MeanSquaredError(), n_particles=5): + def __init__(self, model: keras.models, loss_method=keras.losses.MeanSquaredError(), n_particles: int = 5): """ Initialize the key variables. Args: model : 학습할 모델 객체 (Sequential) loss_method : 손실 함수 - optimizer : 최적화 함수 n_particles(int) : 파티클의 개수 """ - self.model = model # 모델 - self.n_particles = n_particles # 파티클의 개수 - self.loss_method = loss_method # 손실 함수 - self.model_structure = self.model.to_json() # 모델의 구조 - self.init_weights = self.model.get_weights() # 검색할 차원 - self.particle_depth = len(self.model.get_weights()) # 검색할 차원의 깊이 - self.particles_weights = [None] * n_particles # 파티클의 위치 + self.model = model # 모델 + self.n_particles = n_particles # 파티클의 개수 + self.loss_method = loss_method # 손실 함수 + self.model_structure = self.model.to_json() # 모델의 구조 정보 + self.init_weights = self.model.get_weights() # 검색할 차원 + self.particle_depth = len(self.model.get_weights()) # 검색할 차원의 깊이 + self.particles_weights = [None] * n_particles # 파티클의 위치 for _ in tqdm(range(self.n_particles), desc="init particles position"): - # particle_node = [] m = keras.models.model_from_json(self.model_structure) m.compile(loss=self.loss_method, optimizer="adam", metrics=["accuracy"]) self.particles_weights[_] = m.get_weights() - # print(f"shape > {self.particles_weights[_][0]}") - # self.particles_weights.append(particle_node) - - # print(f"particles_weights > {self.particles_weights}") - # self.particles_weights = np.random.uniform(size=(n_particles, self.particle_depth)) \ - # * self.init_pos # 입력받은 파티클의 개수 * 검색할 차원의 크기 만큼의 균등한 위치를 생성 - # self.velocities = [None] * self.n_particles self.velocities = [ [0 for i in range(self.particle_depth)] for n in range(n_particles)] for i in tqdm(range(n_particles), desc="init velocities"): - # print(i) for index, layer in enumerate(self.init_weights): - # print(f"index > {index}") - # print(f"layer > {layer.shape}") self.velocities[i][index] = np.random.rand( *layer.shape) / 5 - 0.10 - # if layer.ndim == 1: - # self.velocities[i][index] = np.random.uniform( - # size=(layer.shape[0],)) - # elif layer.ndim == 2: - # self.velocities[i][index] = np.random.uniform( - # size=(layer.shape[0], layer.shape[1])) - # elif layer.ndim == 3: - # self.velocities[i][index] = np.random.uniform( - # size=(layer.shape[0], layer.shape[1], layer.shape[2])) - # print(f"type > {type(self.velocities)}") - # print(f"velocities > {self.velocities}") - # print(f"velocities > {self.velocities}") - # for i, layer in enumerate(self.init_weights): - # self.velocities[i] = np.random.rand(*layer.shape) / 5 - 0.10 - - # self.velocities = np.random.uniform( - # size=(n_particles, self.particle_depth)) # 입력받은 파티클의 개수 * 검색할 차원의 크기 만큼의 속도를 무작위로 초기화 # 최대 사이즈로 전역 최적갑 저장 - global best - self.g_best = self.model.get_weights() # 전역 최적값(최적의 가중치) self.p_best = self.particles_weights # 각 파티클의 최적값(최적의 가중치) - self.p_best_score = [0 for i in range( - n_particles)] # 각 파티클의 최적값의 점수 - self.g_best_score = 0 # 전역 최적값의 점수(초기화 - 무한대) - self.g_history = [] - self.loss_history = [[] for i in range(n_particles)] - self.acc_history = [[] for i in range(n_particles)] - self.g_best_score_history = [] - self.history = [] + self.g_best = self.model.get_weights() # 전역 최적값(최적의 가중치) | 초기값은 모델의 가중치 + + # 각 파티클의 최적값의 점수 + self.p_best_score = [0 for i in range(n_particles)] + + # 전역 최적값의 점수(초기화 - 0) + self.g_best_score = 0 + self.loss_history = [[] for i in range(n_particles)] # 각 파티클의 손실값 변화 + self.acc_history = [[] for i in range(n_particles)] # 각 파티클의 정확도 변화 + self.g_best_score_history = [] # 전역 최적값의 점수 변화 def _update_weights(self, weights, v): """ @@ -94,11 +69,8 @@ class PSO(object): """ # w = np.array(w) # 각 파티클의 위치 # v = np.array(v) # 각 파티클의 속도(방향과 속력을 가짐) - # print(f"len(w) > {len(w)}") - # print(f"len(v) > {len(v)}") new_weights = [0 for i in range(len(weights))] for i in range(len(weights)): - # print(f"shape > w : {np.shape(w[i])}, v : {np.shape(v[i])}") new_weights[i] = tf.add(weights[i], v[i]) # new_w = tf.add(w, v) # 각 파티클을 랜덤한 속도만큼 진행 return new_weights # 진행한 파티클들의 위치를 반환 @@ -125,18 +97,12 @@ class PSO(object): # 0에서 1사이의 숫자를 랜덤 생성 r0 = np.random.rand() r1 = np.random.rand() - # print(f"type > weights : {type(weights)}") - # print(f"type > v : {type(v)}") - # print( - # f"shape > weights : {np.shape(weights[0])}, v : {np.shape(v[0])}") - # print(f"len > weights : {len(weights)}, v : {len(v)}") # p_best = np.array(p_best) # g_best = np.array(g_best) # 가중치(상수)*속도 + \ # 스케일링 상수*랜덤 가중치*(나의 최적값 - 처음 위치) + \ # 전역 스케일링 상수*랜덤 가중치*(전체 최적값 - 처음 위치) - # for i, layer in enumerate(weights): new_velocity = [None] * len(weights) for i, layer in enumerate(weights): @@ -145,18 +111,6 @@ class PSO(object): new_v = new_v + c1*r1*(self.g_best[i] - layer) new_velocity[i] = new_v - # m2 = tf.multiply(tf.multiply(c0, r0), - # tf.subtract(p_best[i], layer)) - # m3 = tf.multiply(tf.multiply(c1, r1), - # tf.subtract(g_best[i], layer)) - # new_v[i] = tf.add(m1, tf.add(m2, m3)) - # new_v[i] = tf.add_n([m1, m2, m3]) - # new_v[i] = tf.add_n( - # tf.multiply(w, v[i]), - # tf.multiply(tf.multiply(c0, r0), - # tf.subtract(p_best[i], layer)), - # tf.multiply(tf.multiply(c1, r1), - # tf.subtract(g_best[i], layer))) # new_v = w*v + c0*r0*(p_best - weights) + c1*r1*(g_best - weights) return new_velocity @@ -170,13 +124,11 @@ class PSO(object): Returns: (array-like) : 추론에 대한 점수 """ - # = self.model - # model.set_weights(weights) score = self.model.evaluate(x, y, verbose=0) return score - def optimize(self, x_train, y_train, x_test, y_test, maxiter=10, c0=0.5, c1=1.5, w=0.75): + def optimize(self, x_, y_, maxiter=10, c0=0.5, c1=1.5, w=0.75): """ Run the PSO optimization process utill the stoping critera is met. Cas for minization. The aim is to minimize the cost function @@ -188,9 +140,8 @@ class PSO(object): The best solution found (array-like) """ for _ in range(maxiter): - loss = 0 - acc = 0 - for i in tqdm(range(self.n_particles), desc=f"Iter {_}/{maxiter}", ascii=True): + + for i in tqdm(range(self.n_particles), desc=f"Iter {_}/{maxiter} ", ascii=True): weights = self.particles_weights[i] # 각 파티클 추출 v = self.velocities[i] # 각 파티클의 다음 속도 추출 p_best = self.p_best[i] # 결과치 저장할 변수 지정 @@ -200,22 +151,14 @@ class PSO(object): # 다음에 움직일 속도 = 최초 위치, 현재 속도, 현재 위치, 최종 위치 # 3. 위치 업데이트 self.particles_weights[i] = self._update_weights(weights, v) - # 현재 위치 = 최초 위치 현재 속도 - # Update the besst position for particle i + # 현재 위치 = 이전 위치 + 현재 속도 # 내 현재 위치가 내 위치의 최소치보다 작으면 갱신 self.model.set_weights(self.particles_weights[i].copy()) - # self.model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, - # verbose=0, validation_data=(x_test, y_test)) # self.particles_weights[i] = self.model.get_weights() # 4. 평가 self.model.compile(loss=self.loss_method, optimizer='adam', metrics=['accuracy']) - score = self._get_score(x_test, y_test) - # print(score) - - # print(f"score : {score}") - # print(f"loss : {loss}") - # print(f"p_best_score : {self.p_best_score[i]}") + score = self._get_score(x_, y_) if score[1] > self.p_best_score[i]: self.p_best_score[i] = score[1] @@ -223,54 +166,33 @@ class PSO(object): if score[1] > self.g_best_score: self.g_best_score = score[1] self.g_best = self.particles_weights[i].copy() - self.g_history.append(self.g_best.copy()) self.g_best_score_history.append( self.g_best_score) - self.score = score self.loss_history[i].append(score[0]) self.acc_history[i].append(score[1]) - # if self.func(self.particles_weights[i]) < self.func(p_best): - # self.p_best[i] = self.particles_weights[i] - # if self. - # Update the best position overall - # 내 현재 위치가 전체 위치 최소치보다 작으면 갱신 - # if self.func(self.particles_weights[i]) < self.func(self.g_best): - # self.g_best = self.particles_weights[i] - # self.g_history.append(self.g_best) - # print(f"{i} particle score : {score[0]}") - print( - f"loss avg : {self.score[0]/self.n_particles} | acc avg : {self.score[1]/self.n_particles} | best score : {self.g_best_score}") - # self.history.append(self.particles_weights.copy()) + print( + f"loss avg : {score[0]/self.n_particles} | acc avg : {score[1]/self.n_particles} | best score : {self.g_best_score}") # 전체 최소 위치, 전체 최소 벡터 - return self.g_best, self._get_score(x_test, y_test) + return self.g_best, self._get_score(x_, y_) """ Returns: - 현재 전체 위치 + 최종 가중치 """ - def position(self): - return self.particles_weights.copy() + def best_weights(self): + return self.g_best """ Returns: - 전체 위치 벡터 history + 최종 가중치의 스코어 """ - def position_history(self): - return self.history.copy() - - """ - Returns: - global best 의 갱신된 값의 변화를 반환 - """ - - def global_history(self): - return self.g_history.copy() - + def best_score(self): + return self.g_best_score """ Returns: global best score 의 갱신된 값의 변화를 반환 @@ -279,5 +201,10 @@ class PSO(object): def global_score_history(self): return self.g_best_score_history.copy() + """ + Returns: + 모든 파티클의 손실값과 정확도의 변화를 반환 + """ + def all_history(self): return self.loss_history, self.acc_history.copy() diff --git a/mnist.ipynb b/mnist.ipynb deleted file mode 100644 index 8727d8a..0000000 --- a/mnist.ipynb +++ /dev/null @@ -1,1031 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "8a637c69-9071-4012-ac1e-93037548b3e9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-05-24 15:37:52.889357: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.10.0\n", - "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n" - ] - } - ], - "source": [ - "import os\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", - "\n", - "import tensorflow as tf\n", - "# tf.random.set_seed(777) # for reproducibility\n", - "\n", - "from tensorflow import keras\n", - "from keras.datasets import mnist\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Dropout, Flatten\n", - "from keras.layers import Conv2D, MaxPooling2D\n", - "from keras import backend as K\n", - "\n", - "from pso_tf import PSO\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from datetime import date\n", - "from tqdm import tqdm\n", - "import json\n", - "\n", - "print(tf.__version__)\n", - "print(tf.config.list_physical_devices())\n", - "\n", - "def get_data():\n", - " (x_train, y_train), (x_test, y_test) = mnist.load_data()\n", - "\n", - " x_train, x_test = x_train / 255.0, x_test / 255.0\n", - " x_train = x_train.reshape((60000, 28 ,28, 1))\n", - " x_test = x_test.reshape((10000, 28 ,28, 1))\n", - "\n", - " print(f\"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}\")\n", - " print(f\"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}\")\n", - " return x_train, y_train, x_test, y_test\n", - "\n", - "def make_model():\n", - " model = Sequential()\n", - " model.add(Conv2D(32, kernel_size=(5, 5), activation='relu', input_shape=(28,28,1)))\n", - " model.add(MaxPooling2D(pool_size=(3, 3)))\n", - " model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n", - " model.add(MaxPooling2D(pool_size=(2, 2)))\n", - " model.add(Dropout(0.25))\n", - " model.add(Flatten())\n", - " model.add(Dense(128, activation='relu'))\n", - " model.add(Dense(10, activation='softmax'))\n", - "\n", - " # model.summary()\n", - "\n", - " return model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a2d9891d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x_train : (28, 28, 1) | y_train : ()\n", - "x_test : (28, 28, 1) | y_test : ()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "init particles position: 100%|██████████| 30/30 [00:00<00:00, 36.65it/s]\n", - "init velocities: 100%|██████████| 30/30 [00:00<00:00, 681.12it/s]\n", - "Iter 0/20: 13%|#3 | 4/30 [00:04<00:20, 1.28it/s]" - ] - } - ], - "source": [ - "'''\n", - "optimizer parameter\n", - "'''\n", - "lr = 0.1\n", - "momentun = 0.8\n", - "decay = 1e-04\n", - "nestrov = True\n", - "\n", - "'''\n", - "pso parameter\n", - "'''\n", - "n_particles = 30\n", - "maxiter = 20\n", - "# epochs = 1\n", - "w = 0.8\n", - "c0 = 0.6\n", - "c1 = 1.6\n", - "\n", - "\n", - "x_train, y_train, x_test, y_test = get_data()\n", - "model = make_model()\n", - "\n", - "loss = keras.losses.MeanSquaredError()\n", - "\n", - "\n", - "pso_m = PSO(model=model, loss_method=loss, n_particles=n_particles)\n", - "# c0 : 지역 최적값 중요도\n", - "# c1 : 전역 최적값 중요도\n", - "# w : 관성 (현재 속도를 유지하는 정도)\n", - "best_weights, score = pso_m.optimize(x_train, y_train, x_test, y_test, maxiter=maxiter, c0=c0, c1=c1, w=w)\n", - "model.set_weights(best_weights)\n", - "\n", - "score_ = model.evaluate(x_test, y_test, verbose=2)\n", - "print(f\" Test loss: {score_}\")\n", - "score = round(score_[1]*100, 2)\n", - "\n", - "day = date.today().strftime(\"%Y-%m-%d\")\n", - "\n", - "os.makedirs(f'./model', exist_ok=True)\n", - "model.save(f'./model/{day}_{score}_mnist.h5')\n", - "json_save = {\n", - " \"name\" : f\"{day}_{score}_mnist.h5\",\n", - " \"score\" : score_,\n", - " \"maxiter\" : maxiter,\n", - " \"c0\" : c0,\n", - " \"c1\" : c1,\n", - " \"w\" : w \n", - "}\n", - "with open(f'./model/{day}_{score}_mnist.json', 'a') as f:\n", - " json.dump(json_save, f)\n", - " f.write(',\\n')\n", - "\n", - "\n", - "# auto_tuning(n_particles=30, maxiter=1000, c0=0.5, c1=1.5, w=0.75)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1af7569b", - "metadata": {}, - "outputs": [], - "source": [ - "loss_, acc_ = pso_m.all_history()\n", - "\n", - "plt.subplot(2,1,1)\n", - "for layer in all_loss:\n", - " plt.plot(layer)\n", - "plt.title('loss history')\n", - "\n", - "plt.subplot(2,1,2)\n", - "for layer in all_acc:\n", - " plt.plot(layer)\n", - "plt.title('acc history')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1a38f3c1-8291-40d9-838e-4ffbf4578be5", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x_train : (28, 28, 1) | y_train : ()\n", - "x_test : (28, 28, 1) | y_test : ()\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/pieroot/miniconda3/envs/pso/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/gradient_descent.py:111: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n", - " super().__init__(name, **kwargs)\n", - "init particles position: 100%|██████████| 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avg : 0.911343765258789 | acc avg : 0.0045466666420300806 | best loss : 0.20180000364780426\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Iter 45/50: 100%|##########| 30/30 [00:12<00:00, 2.36it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.911343765258789 | acc avg : 0.004050000011920929 | best loss : 0.20180000364780426\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Iter 46/50: 100%|##########| 30/30 [00:11<00:00, 2.58it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.911343765258789 | acc avg : 0.0037399999797344207 | best loss : 0.20180000364780426\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Iter 47/50: 100%|##########| 30/30 [00:11<00:00, 2.60it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.911343765258789 | acc avg : 0.00264999990661939 | best loss : 0.20180000364780426\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Iter 48/50: 100%|##########| 30/30 [00:11<00:00, 2.61it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.911343765258789 | acc avg : 0.0029666667183240254 | best loss : 0.20180000364780426\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Iter 49/50: 100%|##########| 30/30 [00:11<00:00, 2.56it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.911343765258789 | acc avg : 0.002906666696071625 | best loss : 0.20180000364780426\n", - "313/313 - 0s - loss: 27.3092 - accuracy: 0.2018 - 247ms/epoch - 788us/step\n", - " Test loss: [27.309202194213867, 0.20180000364780426]\n", - "x_train : (28, 28, 1) | y_train : ()\n", - "x_test : (28, 28, 1) | y_test : ()\n", - "313/313 [==============================] - 0s 691us/step\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "진행도: 100%|██████████| 10000/10000 [00:00<00:00, 2226867.00it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "틀린 갯수 > 7982/10000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# print(f\"정답 > {y_test}\")\n", - "\n", - "x_train, y_train, x_test, y_test = get_data()\n", - "\n", - "predicted_result = model.predict(x_test)\n", - "predicted_labels = np.argmax(predicted_result, axis=1)\n", - "not_correct = []\n", - "for i in tqdm(range(len(y_test)), desc=\"진행도\"):\n", - " if predicted_labels[i] != y_test[i]:\n", - " not_correct.append(i)\n", - " # print(f\"추론 > {predicted_labels[i]} | 정답 > {y_test[i]}\")\n", - " \n", - "print(f\"틀린 갯수 > {len(not_correct)}/{len(y_test)}\")\n", - "\n", - "\n", - "for i in range(3):\n", - " plt.imshow(x_test[not_correct[i]].reshape(28,28), cmap='Greys')\n", - "plt.show() \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "fc2d7044", - "metadata": {}, - "outputs": [], - "source": [ - "def default_mnist(epochs=5):\n", - " x_train, y_train, x_test, y_test = get_data()\n", - " model = make_model()\n", - " \n", - " model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])\n", - " wei = model.get_weights()\n", - " model.set_weights(wei)\n", - " score = model.evaluate(x_test, y_test, verbose=2)\n", - " print(f\"score : {score}\")\n", - " # hist = model.fit(x_train, y_train, epochs=epochs, batch_size=32, verbose=1)\n", - " # print(hist.history['loss'][-1])\n", - " # print(hist.history['accuracy'][-1])\n", - "\n", - " # predicted_result = model.predict(x_test)\n", - " # predicted_labels = np.argmax(predicted_result, axis=1)\n", - " # not_correct = []\n", - " # for i in tqdm(range(len(y_test)), desc=\"진행도\"):\n", - " # if predicted_labels[i] != y_test[i]:\n", - " # not_correct.append(i)\n", - " # print(f\"추론 > {predicted_labels[i]} | 정답 > {y_test[i]}\")\n", - " \n", - " # print(f\"틀린 갯수 > {len(not_correct)}/{len(y_test)}\")\n", - "# default_mnist()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27024a0b", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pso", - "language": "python", - "name": "pso" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/mnist.py b/mnist.py index 0067710..da4a6c9 100644 --- a/mnist.py +++ b/mnist.py @@ -3,7 +3,7 @@ import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' import tensorflow as tf -# tf.random.set_seed(777) # for reproducibility +tf.random.set_seed(777) # for reproducibility from tensorflow import keras from keras.datasets import mnist @@ -12,32 +12,43 @@ from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras import backend as K -from pso_tf import PSO +# from pso_tf import PSO +from pso import Optimizer +# from optimizer import Optimizer import numpy as np -import matplotlib.pyplot as plt from datetime import date from tqdm import tqdm -import json + +import gc print(tf.__version__) print(tf.config.list_physical_devices()) +print(f"Num GPUs Available: {len(tf.config.list_physical_devices('GPU'))}") + def get_data(): (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 - x_train = x_train.reshape((60000, 28 ,28, 1)) - x_test = x_test.reshape((10000, 28 ,28, 1)) + x_train = x_train.reshape((60000, 28, 28, 1)) + x_test = x_test.reshape((10000, 28, 28, 1)) print(f"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}") print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}") return x_train, y_train, x_test, y_test +def get_data_test(): + (x_train, y_train), (x_test, y_test) = mnist.load_data() + x_test = x_test.reshape((10000, 28, 28, 1)) + + return x_test, y_test + def make_model(): model = Sequential() - model.add(Conv2D(32, kernel_size=(5, 5), activation='relu', input_shape=(28,28,1))) + model.add(Conv2D(32, kernel_size=(5, 5), + activation='relu', input_shape=(28, 28, 1))) model.add(MaxPooling2D(pool_size=(3, 3))) model.add(Conv2D(64, kernel_size=(3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) @@ -50,87 +61,28 @@ def make_model(): return model -# %% -''' -optimizer parameter -''' -lr = 0.1 -momentun = 0.8 -decay = 1e-04 -nestrov = True - -''' -pso parameter -''' -n_particles = 30 -maxiter = 50 -# epochs = 1 -w = 0.8 -c0 = 0.6 -c1 = 1.6 - -def auto_tuning(n_particles=n_particles, maxiter=maxiter, c0=c0, c1=c1, w=w): - x_train, y_train, x_test, y_test = get_data() - model = make_model() - - loss = keras.losses.MeanSquaredError() - optimizer = keras.optimizers.SGD(lr=lr, momentum=momentun, decay=decay, nesterov=nestrov) - - - pso_m = PSO(model=model, loss_method=loss, n_particles=n_particles) - # c0 : 지역 최적값 중요도 - # c1 : 전역 최적값 중요도 - # w : 관성 (현재 속도를 유지하는 정도) - best_weights, score = pso_m.optimize(x_train, y_train, x_test, y_test, maxiter=maxiter, c0=c0, c1=c1, w=w) - model.set_weights(best_weights) - - score_ = model.evaluate(x_test, y_test, verbose=2) - print(f" Test loss: {score_}") - score = round(score_[1]*100, 2) - - day = date.today().strftime("%Y-%m-%d") - - os.makedirs(f'./model', exist_ok=True) - model.save(f'./model/{day}_{score}_mnist.h5') - json_save = { - "name" : f"{day}_{score}_mnist.h5", - "score" : score_, - "maxiter" : maxiter, - "c0" : c0, - "c1" : c1, - "w" : w - } - with open(f'./model/{day}_{score}_pso_mnist.json', 'a') as f: - json.dump(json_save, f) - f.write(',\n') - - return model - -# auto_tuning(n_particles=30, maxiter=1000, c0=0.5, c1=1.5, w=0.75) - - -# %% -# print(f"정답 > {y_test}") -def get_score(model): - x_train, y_train, x_test, y_test = get_data() - - predicted_result = model.predict(x_test) - predicted_labels = np.argmax(predicted_result, axis=1) - not_correct = [] - for i in tqdm(range(len(y_test)), desc="진행도"): - if predicted_labels[i] != y_test[i]: - not_correct.append(i) - # print(f"추론 > {predicted_labels[i]} | 정답 > {y_test[i]}") - - print(f"틀린 갯수 > {len(not_correct)}/{len(y_test)}") - - # for i in range(3): - # plt.imshow(x_test[not_correct[i]].reshape(28,28), cmap='Greys') - # plt.show() - -get_score(auto_tuning(n_particles=30, maxiter=50, c0=0.5, c1=1.5, w=0.75)) # %% +model = make_model() +x_test, y_test = get_data_test() +# loss = 'binary_crossentropy' +# loss = 'categorical_crossentropy' +# loss = 'sparse_categorical_crossentropy' +# loss = 'kullback_leibler_divergence' +# loss = 'poisson' +# loss = 'cosine_similarity' +# loss = 'log_cosh' +# loss = 'huber_loss' +# loss = 'mean_absolute_error' +# loss = 'mean_absolute_percentage_error' +loss = 'mean_squared_error' +pso_mnist = Optimizer(model, loss=loss, n_particles=50, c0=0.4, c1=0.8, w_min=0.75, w_max=1.4) +weight, score = pso_mnist.fit( + x_test, y_test, epochs=1000, save=True, save_path="./result/mnist", renewal="acc", empirical_balance=False, Dispersion=True) +pso_mnist.model_save("./result/mnist") +pso_mnist.save_info("./result/mnist") + +gc.collect() diff --git a/plt.ipynb b/plt.ipynb new file mode 100644 index 0000000..1be1066 --- /dev/null +++ b/plt.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from time import sleep\n", + "import gc" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(27, 100)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "15762" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris = pd.read_csv(\"./result/iris/05-29-03-49_50_500_0.4_0.8_0.7_acc.csv\", header=None)\n", + "print(iris.shape)\n", + "# print(iris.head)\n", + "loss = []\n", + "acc = []\n", + "for i in range(len(iris.iloc[0])):\n", + " if i % 2 == 0:\n", + " loss.append(iris[i])\n", + " else:\n", + " acc.append(iris[i])\n", + "\n", + "plt.subplot(2,1,1)\n", + "plt.grid()\n", + "plt.ylabel(\"loss\")\n", + "plt.title(f\"loss and acc\")\n", + "for i in range(len(loss)):\n", + " plt.plot(loss[i], label=f\"loss_{i}\")\n", + "\n", + "plt.subplot(2,1,2)\n", + "plt.grid()\n", + "plt.xlabel(\"epoch\")\n", + "\n", + "plt.ylabel(\"acc\")\n", + "for i in range(len(acc)):\n", + " plt.plot(acc[i], label=f\"acc_{i}\")\n", + "\n", + "plt.show()\n", + "plt.clf()\n", + "plt.clf()\n", + "plt.close()\n", + "\n", + "gc.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pso", + "language": "python", + "name": "pso" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pso/__init__.py b/pso/__init__.py new file mode 100644 index 0000000..92112d2 --- /dev/null +++ b/pso/__init__.py @@ -0,0 +1,5 @@ +from .optimizer import Optimizer + +__all__ = [ + 'Optimizer' +] \ No newline at end of file diff --git a/pso/optimizer.py b/pso/optimizer.py new file mode 100644 index 0000000..9ceeecf --- /dev/null +++ b/pso/optimizer.py @@ -0,0 +1,303 @@ +import os + +import tensorflow as tf +from tensorflow import keras + +import numpy as np + +# import cupy as cp + +from tqdm import tqdm +from datetime import datetime +import json +import gc + +from pso.particle import Particle + + +class Optimizer: + def __init__( + self, + model: keras.models, + loss = "mse", + n_particles: int = 10, + c0=0.5, + c1=1.5, + w_min=0.5, + w_max=1.5, + ): + self.model = model # 모델 구조 + self.loss = loss # 손실함수 + self.n_particles = n_particles # 파티클 개수 + self.particles = [None] * n_particles # 파티클 리스트 + self.c0 = c0 # local rate - 지역 최적값 관성 수치 + self.c1 = c1 # global rate - 전역 최적값 관성 수치 + self.w_min = w_min # 최소 관성 수치 + self.w_max = w_max # 최대 관성 수치 + + self.g_best_score = 0 # 최고 점수 - 시작은 0으로 초기화 + self.g_best = None # 최고 점수를 받은 가중치 + self.g_best_ = None # 최고 점수를 받은 가중치 - 값의 분산을 위한 변수 + + for i in tqdm(range(self.n_particles), desc="Initializing Particles"): + m = keras.models.model_from_json(model.to_json()) + m.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"]) + + self.particles[i] = Particle(m, loss) + + """ + Returns: + (cupy array) : 가중치 - 1차원으로 풀어서 반환 + (list) : 가중치의 원본 shape + (list) : 가중치의 원본 shape의 길이 + """ + + def _encode(self, weights): + # w_gpu = cp.array([]) + w_gpu = np.array([]) + lenght = [] + shape = [] + for layer in weights: + shape.append(layer.shape) + w_ = layer.reshape(-1) + lenght.append(len(w_)) + # w_gpu = cp.append(w_gpu, w_) + w_gpu = np.append(w_gpu, w_) + + return w_gpu, shape, lenght + + """ + Returns: + (list) : 가중치 원본 shape으로 복원 + """ + + def _decode(self, weight, shape, lenght): + weights = [] + start = 0 + for i in range(len(shape)): + end = start + lenght[i] + w_ = weight[start:end] + # w_ = weight[start:end].get() + w_ = np.reshape(w_, shape[i]) + # w_ = w_.reshape(shape[i]) + weights.append(w_) + start = end + del weight + del shape + del lenght + gc.collect() + + return weights + + def f(self, x, y, weights): + self.model.set_weights(weights) + self.model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"]) + score = self.model.evaluate(x, y, verbose=0)[1] + if score > 0: + return 1 / (1 + score) + else: + return 1 + np.abs(score) + + """ + parameters + ---------- + x : numpy.ndarray + y : numpy.ndarray + epochs : int + save : bool + save_path : str ex) "./result" + renewal : str ex) "acc" or "loss" + """ + + """ + parameters + fit( + x_test : numpy.ndarray, + y_test : numpy.ndarray, + epochs : int, + save : bool - True : save, False : not save + save_path : str ex) "./result", + renewal : str ex) "acc" or "loss", + empirical_balance : bool - True : empirical balance, False : no balance + Dispersion : bool - True : random search, False : PSO + """ + def fit( + self, + x, + y, + epochs: int = 100, + save: bool = False, + save_path: str = "./result", + renewal: str = "acc", + empirical_balance: bool = False, + Dispersion: bool = False, + check_point: int = None, + ): + self.renewal = renewal + if renewal == "acc": + self.g_best_score = 0 + elif renewal == "loss": + self.g_best_score = np.inf + + if save: + if save_path is None: + raise ValueError("save_path is None") + else: + self.save_path = save_path + os.makedirs(save_path, exist_ok=True) + self.day = datetime.now().strftime("%m-%d-%H-%M") + + for i, p in enumerate(self.particles): + local_score = p.get_score(x, y, renewal=renewal) + + if renewal == "acc": + if local_score[1] > self.g_best_score: + self.g_best_score = local_score[1] + self.g_best = p.get_best_weights() + self.g_best_ = p.get_best_weights() + elif renewal == "loss": + if local_score[0] < self.g_best_score: + self.g_best_score = local_score[0] + self.g_best = p.get_best_weights() + self.g_best_ = p.get_best_weights() + + print(f"initial g_best_score : {self.g_best_score}") + + for _ in range(epochs): + acc = 0 + loss = 0 + min_score = np.inf + max_score = 0 + min_loss = np.inf + max_loss = 0 + + # for i in tqdm(range(len(self.particles)), desc=f"epoch {_ + 1}/{epochs}", ascii=True): + for i in range(len(self.particles)): + w = self.w_min + (self.w_max - self.w_min) * _ / epochs + + if Dispersion: + g_best = self.g_best_ + else: + g_best = self.g_best + + if empirical_balance: + if np.random.rand() < np.exp(-(_) / epochs): + w_p_ = self.f(x, y, self.particles[i].get_best_weights()) + w_g_ = self.f(x, y, self.g_best) + w_p = w_p_ / (w_p_ + w_g_) + w_g = w_p_ / (w_p_ + w_g_) + + else: + p = 1 / (self.n_particles * np.linalg.norm(self.c1 - self.c0)) + p = np.exp(-p) + w_p = p + w_g = 1 - p + + score = self.particles[i].step_w( + x, y, self.c0, self.c1, w, g_best, w_p, w_g, renewal=renewal + ) + + else: + score = self.particles[i].step( + x, y, self.c0, self.c1, w, g_best, renewal=renewal + ) + + if renewal == "acc": + if score[1] >= self.g_best_score: + self.g_best_score = score[1] + self.g_best = self.particles[i].get_best_weights() + elif renewal == "loss": + if score[0] <= self.g_best_score: + self.g_best_score = score[0] + self.g_best = self.particles[i].get_best_weights() + + loss += score[0] + acc += score[1] + if score[0] < min_loss: + min_loss = score[0] + if score[0] > max_loss: + max_loss = score[0] + + if score[1] < min_score: + min_score = score[1] + if score[1] > max_score: + max_score = score[1] + + if save: + with open( + f"./{save_path}/{self.day}_{self.n_particles}_{epochs}_{self.c0}_{self.c1}_{self.w_min}_{renewal}.csv", + "a", + ) as f: + f.write(f"{score[0]}, {score[1]}") + if i != self.n_particles - 1: + f.write(", ") + + TS = self.c0 + np.random.rand() * (self.c1 - self.c0) + g_, g_sh, g_len = self._encode(self.g_best) + decrement = (epochs - (_) + 1) / epochs + g_ = (1 - decrement) * g_ + decrement * TS + self.g_best_ = self._decode(g_, g_sh, g_len) + + if save: + with open( + f"./{save_path}/{self.day}_{self.n_particles}_{epochs}_{self.c0}_{self.c1}_{self.w_min}_{renewal}.csv", + "a", + ) as f: + f.write("\n") + + print(f"epoch {_ + 1}/{epochs} finished") + # print(f"loss min : {min_loss} | loss max : {max_loss} | acc min : {min_score} | acc max : {max_score}") + # print(f"loss avg : {loss/self.n_particles} | acc avg : {acc/self.n_particles} | Best {renewal} : {self.g_best_score}") + print( + f"loss min : {min_loss} | acc avg : {max_score} | Best {renewal} : {self.g_best_score}" + ) + + gc.collect() + + if check_point is not None: + if _ % check_point == 0: + self._check_point_save(f"./{save_path}/{self.day}/check_point_{_}.h5") + + return self.g_best, self.g_best_score + + def get_best_model(self): + model = keras.models.model_from_json(self.model.to_json()) + model.set_weights(self.g_best) + model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"]) + return model + + def get_best_score(self): + return self.g_best_score + + def get_best_weights(self): + return self.g_best + + def save_info(self, path: str = "./result"): + json_save = { + "name": f"{self.day}_{self.n_particles}_{self.c0}_{self.c1}_{self.w_min}.h5", + "n_particles": self.n_particles, + "score": self.g_best_score, + "c0": self.c0, + "c1": self.c1, + "w_min": self.w_min, + "w_max": self.w_max, + "loss_method": self.loss, + "renewal": self.renewal, + } + + with open( + f"./{path}/{self.day}_{self.loss}_{self.n_particles}_{self.g_best_score}.json", + "w", + ) as f: + json.dump(json_save, f, indent=4) + + def _check_point_save(self, save_path: str = f"./result/check_point"): + model = self.get_best_model() + model.save(save_path) + + def model_save(self, save_path: str = "./result/model"): + model = self.get_best_model() + model.save( + f"./{save_path}/{self.day}/{self.n_particles}_{self.c0}_{self.c1}_{self.w_min}.h5" + ) + return model diff --git a/pso/particle.py b/pso/particle.py new file mode 100644 index 0000000..e9493ac --- /dev/null +++ b/pso/particle.py @@ -0,0 +1,124 @@ + +import tensorflow as tf +from tensorflow import keras + +# import cupy as cp +import numpy as np + +class Particle: + def __init__(self, model:keras.models, loss): + self.model = model + self.loss = loss + self.init_weights = self.model.get_weights() + i_w_,s_,l_ = self._encode(self.init_weights) + i_w_ = np.random.rand(len(i_w_)) / 5 - 0.10 + self.velocities = self._decode(i_w_,s_,l_) + + self.best_score = 0 + self.best_weights = self.init_weights + + + """ + Returns: + (cupy array) : 가중치 - 1차원으로 풀어서 반환 + (list) : 가중치의 원본 shape + (list) : 가중치의 원본 shape의 길이 + """ + def _encode(self, weights:list): + # w_gpu = cp.array([]) + w_gpu = np.array([]) + lenght = [] + shape = [] + for layer in weights: + shape.append(layer.shape) + w_ = layer.reshape(-1) + lenght.append(len(w_)) + # w_gpu = cp.append(w_gpu, w_) + w_gpu = np.append(w_gpu, w_) + + return w_gpu, shape, lenght + + """ + Returns: + (list) : 가중치 원본 shape으로 복원 + """ + + def _decode(self, weight:list, shape, lenght): + weights = [] + start = 0 + for i in range(len(shape)): + end = start + lenght[i] + w_ = weight[start:end] + # w_ = weight[start:end].get() + w_ = np.reshape(w_, shape[i]) + # w_ = w_.reshape(shape[i]) + weights.append(w_) + start = end + + return weights + + def get_score(self, x, y, renewal:str = "acc"): + self.model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"]) + score = self.model.evaluate(x, y, verbose=0) + # print(score) + if renewal == "acc": + if score[1] > self.best_score: + self.best_score = score[1] + self.best_weights = self.model.get_weights() + elif renewal == "loss": + if score[0] < self.best_score: + self.best_score = score[0] + self.best_weights = self.model.get_weights() + + return score + def _update_velocity(self, local_rate, global_rate, w, g_best): + encode_w, w_sh, w_len = self._encode(weights = self.model.get_weights()) + encode_v, _, _ = self._encode(weights = self.velocities) + encode_p, _, _ = self._encode(weights = self.best_weights) + encode_g, _, _ = self._encode(weights = g_best) + r0 = np.random.rand() + r1 = np.random.rand() + new_v = w * encode_v + local_rate * r0 * (encode_p - encode_w) + global_rate * r1 * (encode_g - encode_w) + self.velocities = self._decode(new_v, w_sh, w_len) + + def _update_velocity_w(self, local_rate, global_rate, w, w_p, w_g, g_best): + encode_w, w_sh, w_len = self._encode(weights = self.model.get_weights()) + encode_v, _, _ = self._encode(weights = self.velocities) + encode_p, _, _ = self._encode(weights = self.best_weights) + encode_g, _, _ = self._encode(weights = g_best) + r0 = np.random.rand() + r1 = np.random.rand() + new_v = w * encode_v + local_rate * r0 * (w_p * encode_p - encode_w) + global_rate * r1 * (w_g * encode_g - encode_w) + self.velocities = self._decode(new_v, w_sh, w_len) + + def _update_weights(self): + encode_w, w_sh, w_len = self._encode(weights = self.model.get_weights()) + encode_v, _, _ = self._encode(weights = self.velocities) + new_w = encode_w + encode_v + self.model.set_weights(self._decode(new_w, w_sh, w_len)) + + + def f(self, x, y, weights): + self.model.set_weights(weights) + score = self.model.evaluate(x, y, verbose = 0)[1] + if score > 0: + return 1 / (1 + score) + else: + return 1 + np.abs(score) + + def step(self, x, y, local_rate, global_rate, w, g_best, renewal:str = "acc"): + self._update_velocity(local_rate, global_rate, w, g_best) + self._update_weights() + return self.get_score(x, y, renewal) + + def step_w(self, x, y, local_rate, global_rate, w, g_best, w_p, w_g, renewal:str = "acc"): + self._update_velocity_w(local_rate, global_rate, w, w_p, w_g, g_best) + self._update_weights() + return self.get_score(x, y, renewal) + + + def get_best_score(self): + return self.best_score + + def get_best_weights(self): + return self.best_weights \ No newline at end of file diff --git a/pso_tuning.py b/pso_tuning.py deleted file mode 100644 index fa267ce..0000000 --- a/pso_tuning.py +++ /dev/null @@ -1,155 +0,0 @@ -# %% -import json -from tqdm import tqdm -from datetime import date -import matplotlib.pyplot as plt -import numpy as np -from PSO.pso_bp import PSO -from keras import backend as K -from keras.layers import Conv2D, MaxPooling2D -from keras.layers import Dense, Dropout, Flatten -from keras.models import Sequential -from keras.datasets import mnist -from tensorflow import keras -import tensorflow as tf -import os -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' - -tf.random.set_seed(777) # for reproducibility - - -print(tf.__version__) -print(tf.config.list_physical_devices()) - - -def get_data(): - (x_train, y_train), (x_test, y_test) = mnist.load_data() - - x_train, x_test = x_train / 255.0, x_test / 255.0 - x_train = x_train.reshape((60000, 28, 28, 1)) - x_test = x_test.reshape((10000, 28, 28, 1)) - - print(f"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}") - print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}") - return x_train, y_train, x_test, y_test - - -def make_model(): - model = Sequential() - model.add(Conv2D(32, kernel_size=(5, 5), - activation='relu', input_shape=(28, 28, 1))) - model.add(MaxPooling2D(pool_size=(3, 3))) - model.add(Conv2D(64, kernel_size=(3, 3), activation='relu')) - model.add(MaxPooling2D(pool_size=(2, 2))) - model.add(Dropout(0.25)) - model.add(Flatten()) - model.add(Dense(128, activation='relu')) - model.add(Dense(10, activation='softmax')) - - model.compile(loss='sparse_categorical_crossentropy', - optimizer='adam', metrics=['accuracy']) - - # model.summary() - - return model - - -# %% -''' -optimizer parameter -''' -lr = 0.1 -momentun = 0.8 -decay = 1e-04 -nestrov = True - -''' -pso parameter -''' -n_particles = 100 -maxiter = 500 -# epochs = 1 -w = 0.8 -c0 = 0.6 -c1 = 1.6 - -def auto_tuning(): - x_train, y_train, x_test, y_test = get_data() - model = make_model() - - loss = keras.losses.MeanSquaredError() - optimizer = keras.optimizers.SGD(lr=lr, momentum=momentun, decay=decay, nesterov=nestrov) - - - pso_m = PSO(model=model, loss_method=loss, n_particles=n_particles) - # c0 : 지역 최적값 중요도 - # c1 : 전역 최적값 중요도 - # w : 관성 (현재 속도를 유지하는 정도) - best_weights, score = pso_m.optimize(x_train, y_train, x_test, y_test, maxiter=maxiter, c0=c0, c1=c1, w=w) - model.set_weights(best_weights) - - score_ = model.evaluate(x_test, y_test, verbose=2) - print(f" Test loss: {score_}") - score = round(score_[0]*100, 2) - - day = date.today().strftime("%Y-%m-%d") - - model.save(f'./model/{day}_{score}_mnist.h5') - json_save = { - "name" : f"{day}_{score}_mnist.h5", - "score" : score_, - "maxiter" : maxiter, - "c0" : c0, - "c1" : c1, - "w" : w - } - with open(f'./model/{day}_{score}_bp_mnist.json', 'a') as f: - json.dump(json_save, f) - f.write(',\n') - - return model -auto_tuning() - - -# %% -# print(f"정답 > {y_test}") -def get_score(model): - x_train, y_train, x_test, y_test = get_data() - - predicted_result = model.predict(x_test) - predicted_labels = np.argmax(predicted_result, axis=1) - not_correct = [] - for i in tqdm(range(len(y_test)), desc="진행도"): - if predicted_labels[i] != y_test[i]: - not_correct.append(i) - # print(f"추론 > {predicted_labels[i]} | 정답 > {y_test[i]}") - - print(f"틀린 갯수 > {len(not_correct)}/{len(y_test)}") - - for i in range(3): - plt.imshow(x_test[not_correct[i]].reshape(28, 28), cmap='Greys') - plt.show() - -# %% - - -def default_mnist(epochs=5): - x_train, y_train, x_test, y_test = get_data() - model = make_model() - - hist = model.fit(x_train, y_train, epochs=epochs, batch_size=32, verbose=1) - print(hist.history['loss'][-1]) - print(hist.history['accuracy'][-1]) - - predicted_result = model.predict(x_test) - predicted_labels = np.argmax(predicted_result, axis=1) - not_correct = [] - for i in tqdm(range(len(y_test)), desc="진행도"): - if predicted_labels[i] != y_test[i]: - not_correct.append(i) - # print(f"추론 > {predicted_labels[i]} | 정답 > {y_test[i]}") - - print(f"틀린 갯수 > {len(not_correct)}/{len(y_test)}") - - -# %% diff --git a/psokeras/__init__.py b/psokeras/__init__.py new file mode 100755 index 0000000..b3a9721 --- /dev/null +++ b/psokeras/__init__.py @@ -0,0 +1,14 @@ +# -*- coding: utf-8 -*- +"""PSOkeras - Particle Swarm Optimizer for Keras models + +This module implements a particle swarm optimizer for training the weights of Keras models. The + +""" + + +from .version import __version__ +from .optimizer import Optimizer + +__all__ = [ + 'Optimizer', +] diff --git a/psokeras/optimizer.py b/psokeras/optimizer.py new file mode 100755 index 0000000..b4fbaab --- /dev/null +++ b/psokeras/optimizer.py @@ -0,0 +1,67 @@ +BIG_SCORE = 1.e6 # type: float + +import keras +from psokeras.particle import Particle +from .util import ProgressBar + + +class Optimizer: + def __init__(self, model, loss, + n=10, + acceleration=0.1, + local_rate=1.0, + global_rate=1.0): + + self.n_particles = n + self.structure = model.to_json() + self.particles = [None] * n + self.loss = loss + self.length = len(model.get_weights()) + + params = {'acc': acceleration, 'local_acc': local_rate, 'global_acc': global_rate} + + for i in range(n-1): + m = keras.models.model_from_json(self.structure) + m.compile(loss=loss,optimizer='sgd') + self.particles[i] = Particle(m, params) + + self.particles[n-1] = Particle(model, params) + + self.global_best_weights = None + self.global_best_score = BIG_SCORE + + def fit(self, x, y, steps=0, batch_size=32): + num_batches = x.shape[0] // batch_size + + for i, p in enumerate(self.particles): + local_score = p.get_score(x, y) + + if local_score < self.global_best_score: + self.global_best_score = local_score + self.global_best_weights = p.get_best_weights() + + print("PSO -- Initial best score {:0.4f}".format(self.global_best_score)) + + bar = ProgressBar(steps, updates=20) + + for i in range(steps): + for j in range(num_batches): + x_ = x[j*batch_size:(j+1)*batch_size,:] + y_ = y[j*batch_size:(j+1)*batch_size] + + for p in self.particles: + local_score = p.step(x_, y_, self.global_best_weights) + + if local_score < self.global_best_score: + self.global_best_score = local_score + self.global_best_weights = p.get_best_weights() + + bar.update(i) + + bar.done() + + def get_best_model(self): + best_model = keras.models.model_from_json(self.structure) + best_model.set_weights(self.global_best_weights) + best_model.compile(loss=self.loss,optimizer='sgd') + return best_model diff --git a/psokeras/particle.py b/psokeras/particle.py new file mode 100755 index 0000000..15a9091 --- /dev/null +++ b/psokeras/particle.py @@ -0,0 +1,66 @@ +import random + +import numpy as np + +from psokeras.optimizer import BIG_SCORE + + +class Particle: + def __init__(self, model, params): + self.model = model + self.params = params + self.init_weights = model.get_weights() + self.velocities = [None] * len(self.init_weights) + self.length = len(self.init_weights) + for i, layer in enumerate(self.init_weights): + self.velocities[i] = np.random.rand(*layer.shape) / 5 - 0.10 + # self.velocities[i] = np.zeros(layer.shape) + + self.best_weights = None + self.best_score = BIG_SCORE + + def get_score(self, x, y, update=True): + local_score = self.model.evaluate(x, y, verbose=0) + if local_score < self.best_score and update: + self.best_score = local_score + self.best_weights = self.model.get_weights() + + return local_score + + def _update_velocities(self, global_best_weights, depth): + new_velocities = [None] * len(self.init_weights) + weights = self.model.get_weights() + local_rand, global_rand = random.random(), random.random() + + for i, layer in enumerate(weights): + if i >= depth: + new_velocities[i] = self.velocities[i] + continue + new_v = self.params['acc'] * self.velocities[i] + new_v = new_v + self.params['local_acc'] * local_rand * (self.best_weights[i] - layer) + new_v = new_v + self.params['global_acc'] * global_rand * (global_best_weights[i] - layer) + new_velocities[i] = new_v + + self.velocities = new_velocities + + def _update_weights(self, depth): + old_weights = self.model.get_weights() + new_weights = [None] * len(old_weights) + for i, layer in enumerate(old_weights): + if i>= depth: + new_weights[i] = layer + continue + new_w = layer + self.velocities[i] + new_weights[i] = new_w + + self.model.set_weights(new_weights) + + def step(self, x, y, global_best_weights,depth=None): + if depth is None: + depth = self.length + self._update_velocities(global_best_weights, depth) + self._update_weights(depth) + return self.get_score(x, y) + + def get_best_weights(self): + return self.best_weights diff --git a/psokeras/util.py b/psokeras/util.py new file mode 100755 index 0000000..08ce5ed --- /dev/null +++ b/psokeras/util.py @@ -0,0 +1,31 @@ +class ProgressBar: + def __init__(self, steps, updates=10): + self.step = 0 + self.step_size = (steps // updates) + self.total_steps = steps + self.updates = updates + + bar = self._make_bar(0) + print(bar, end=' ') + + def update(self, i): + if i % self.step_size > 0: + return + + self.step = i // self.step_size + bar = self._make_bar(i) + + print(bar, end=' ') + + def done(self): + self.step = self.total_steps + bar = self._make_bar(self.updates) + print(bar) + + def _make_bar(self, x): + bar = "[" + for x in range(self.updates): + print("\r", end=' ') + bar += "=" if x < self.step else " " + bar += "]" + return bar diff --git a/psokeras/version.py b/psokeras/version.py new file mode 100755 index 0000000..7fd229a --- /dev/null +++ b/psokeras/version.py @@ -0,0 +1 @@ +__version__ = '0.2.0' diff --git a/pyswarms/example.ipynb b/pyswarms/example.ipynb new file mode 100644 index 0000000..95d53ed --- /dev/null +++ b/pyswarms/example.ipynb @@ -0,0 +1,484 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-28 17:28:58.354284: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-05-28 17:28:58.477863: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2023-05-28 17:28:58.851418: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.2/lib64:/usr/local/TensorRT/lib:/usr/local/cuda-11.2/lib64:/usr/local/TensorRT/lib:\n", + "2023-05-28 17:28:58.851559: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.2/lib64:/usr/local/TensorRT/lib:/usr/local/cuda-11.2/lib64:/usr/local/TensorRT/lib:\n", + "2023-05-28 17:28:58.851564: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "import numpy as np\n", + "from IPython.display import Image\n", + "from keras.callbacks import TensorBoard\n", + "from keras.layers import Dense\n", + "from keras.models import Sequential\n", + "from pyswarms.single.global_best import GlobalBestPSO\n", + "from pyswarms.utils.functions import single_obj as fx\n", + "from pyswarms.utils.plotters import plot_surface\n", + "from pyswarms.utils.plotters.formatters import Animator, Designer, Mesher\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "iris = load_iris()\n", + "X = iris['data']\n", + "y = iris['target']\n", + "names = iris['target_names']\n", + "feature_names = iris['feature_names']\n", + "enc = OneHotEncoder()\n", + "Y = enc.fit_transform(y[:, np.newaxis]).toarray()\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "X_train, X_test, Y_train, Y_test = train_test_split(\n", + " X_scaled, Y, test_size=0.5, random_state=2)\n", + "n_features = X.shape[1]\n", + "n_classes = Y.shape[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def create_custom_model(input_dim, output_dim, nodes, n=1, name='model'):\n", + " model = Sequential(name=name)\n", + " for i in range(n):\n", + " model.add(Dense(nodes, input_dim=input_dim, activation='relu'))\n", + " model.add(Dense(output_dim, activation='softmax'))\n", + " model.compile(loss='categorical_crossentropy',\n", + " optimizer='adam',\n", + " metrics=['accuracy'])\n", + " return model\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense (Dense) (None, 4) 20 \n", + " \n", + " dense_1 (Dense) (None, 3) 15 \n", + " \n", + "=================================================================\n", + "Total params: 35\n", + "Trainable params: 35\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-28 17:29:19.512279: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-28 17:29:19.516705: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-28 17:29:19.516924: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-28 17:29:19.517342: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-05-28 17:29:19.517934: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-28 17:29:19.518103: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-28 17:29:19.518250: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-28 17:29:19.891585: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-28 17:29:19.891755: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-28 17:29:19.891875: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-28 17:29:19.891968: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10109 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:09:00.0, compute capability: 8.6\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model name: model\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-28 17:29:20.713676: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3/3 [==============================] - 0s 1ms/step - loss: 0.0960 - accuracy: 0.9600\n", + "Test loss: 0.09600644558668137\n", + "Test accuracy: 0.9599999785423279\n", + "--- 13.446455717086792 seconds ---\n" + ] + } + ], + "source": [ + "n_layers = 1\n", + "model = create_custom_model(n_features, n_classes,\n", + " 4, n_layers)\n", + "model.summary()\n", + "\n", + "start_time = time.time()\n", + "print('Model name:', model.name)\n", + "history_callback = model.fit(X_train, Y_train,\n", + " batch_size=5,\n", + " epochs=400,\n", + " verbose=0,\n", + " validation_data=(X_test, Y_test)\n", + " )\n", + "score = model.evaluate(X_test, Y_test)\n", + "print('Test loss:', score[0])\n", + "print('Test accuracy:', score[1])\n", + "print(\"--- %s seconds ---\" % (time.time() - start_time))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def get_shape(model):\n", + " weights_layer = model.get_weights()\n", + " shapes = []\n", + " for weights in weights_layer:\n", + " shapes.append(weights.shape)\n", + " return shapes\n", + "def set_shape(weights,shapes):\n", + " new_weights = []\n", + " index=0\n", + " for shape in shapes:\n", + " if(len(shape)>1):\n", + " n_nodes = np.prod(shape)+index\n", + " else:\n", + " n_nodes=shape[0]+index\n", + " tmp = np.array(weights[index:n_nodes]).reshape(shape)\n", + " new_weights.append(tmp)\n", + " index=n_nodes\n", + " return new_weights" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-28 17:29:35,386 - pyswarms.single.global_best - INFO - Optimize for 20 iters with {'c1': 0.4, 'c2': 0.6, 'w': 0.4}\n", + "pyswarms.single.global_best: 100%|██████████|20/20, best_cost=0.0133\n", + "2023-05-28 17:30:07,637 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.013333320617675781, best pos: [ 0.17027965 0.17696722 -0.07395054 0.31544984 0.17052408 -0.37810479\n", + " 0.24267479 0.16931148 0.65606942 -0.24207116 -0.66562722 0.02191478\n", + " 0.5870387 0.78966943 -0.4457816 0.0907434 -0.1808341 0.29282655\n", + " 0.61472003 0.90660508 0.16469465 -0.55057763 0.54702005 -0.22636745\n", + " 0.01125538 0.62431828 0.02128613 -0.26723577 -0.43527016 0.51223244\n", + " 0.76388399 -0.02073011 0.15949622 0.45878514 0.01787211]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3/3 [==============================] - 0s 1ms/step - loss: 0.7467 - accuracy: 0.9600\n", + "Test loss: 0.7467350363731384\n", + "Test accuracy: 0.9599999785423279\n", + "--- 32.29235529899597 seconds ---\n" + ] + } + ], + "source": [ + "start_time = time.time()\n", + "def evaluate_nn(W, shape,X_train=X_train, Y_train=Y_train):\n", + " results = []\n", + " for weights in W:\n", + " model.set_weights(set_shape(weights,shape))\n", + " score = model.evaluate(X_train, Y_train, verbose=0)\n", + " results.append(1-score[1])\n", + " return results\n", + "\n", + "shape = get_shape(model)\n", + "x_max = 1.0 * np.ones(35)\n", + "x_min = -1.0 * x_max\n", + "bounds = (x_min, x_max)\n", + "options = {'c1': 0.4, 'c2': 0.6, 'w': 0.4}\n", + "optimizer = GlobalBestPSO(n_particles=50, dimensions=35,\n", + " options=options, bounds=bounds)\n", + "cost, pos = optimizer.optimize(evaluate_nn, 20, X_train=X_train, Y_train=Y_train,shape=shape)\n", + "model.set_weights(set_shape(pos,shape))\n", + "score = model.evaluate(X_test, Y_test)\n", + "print('Test loss:', score[0])\n", + "print('Test accuracy:', score[1])\n", + "print(\"--- %s seconds ---\" % (time.time() - start_time))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-28 17:30:08,140 - matplotlib.animation - WARNING - MovieWriter pillowwritter unavailable; using Pillow instead.\n", + "2023-05-28 17:30:08,141 - matplotlib.animation - INFO - Animation.save using \n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m = Mesher(func=fx.sphere)\n", + "pos_history = [pos[:, :2] for pos in optimizer.pos_history]\n", + "pos3d = m.compute_history_3d(pos_history)\n", + "# Assuming we already had an optimizer ready\n", + "my_animator = Animator(repeat=False)\n", + "my_designer = Designer(figsize=(6, 6))\n", + "animation = plot_surface(pos3d, animator=my_animator, designer=my_designer)\n", + "# %%\n", + "animation.save('pso.gif', writer='pillowwritter', fps=6, )\n", + "Image(url='pso.gif')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from sklearn.model_selection import KFold" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 71ms/step - loss: 0.0138 - accuracy: 1.0000\n", + "1/1 [==============================] - 0s 67ms/step - loss: 0.1226 - accuracy: 0.9000\n", + "1/1 [==============================] - 0s 67ms/step - loss: 0.1448 - accuracy: 0.9333\n", + "WARNING:tensorflow:5 out of the last 3010 calls to .test_function at 0x7fee6622df70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-28 17:31:01,885 - tensorflow - WARNING - 5 out of the last 3010 calls to .test_function at 0x7fee6622df70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 69ms/step - loss: 0.0451 - accuracy: 1.0000\n", + "WARNING:tensorflow:6 out of the last 3011 calls to .test_function at 0x7fee66257b80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-28 17:31:08,794 - tensorflow - WARNING - 6 out of the last 3011 calls to .test_function at 0x7fee66257b80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 68ms/step - loss: 0.0970 - accuracy: 0.9667\n" + ] + } + ], + "source": [ + "X = iris[\"data\"]\n", + "y = iris[\"target\"]\n", + "\n", + "enc = OneHotEncoder()\n", + "Y = enc.fit_transform(y[:, np.newaxis]).toarray()\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "inputs = X_scaled\n", + "targets = Y\n", + "\n", + "num_folds = 5\n", + "\n", + "kfold = KFold(n_splits=num_folds, shuffle=True)\n", + "\n", + "fold_no = 1\n", + "accs_bp = []\n", + "\n", + "x_max = 1.0 * np.ones(35)\n", + "x_min = -1.0 * x_max\n", + "bounds = (x_min, x_max)\n", + "\n", + "for train, test in kfold.split(inputs, targets):\n", + " model = create_custom_model(n_features, n_classes,\n", + " 4, 1)\n", + " shape = get_shape(model)\n", + " history_callback = model.fit(inputs[train], targets[train],\n", + " batch_size=5,\n", + " epochs=400,\n", + " verbose=0,\n", + " )\n", + " score = model.evaluate(inputs[test], targets[test])\n", + " fold_no += 1\n", + " accs_bp.append(score[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 14ms/step - loss: 0.5820 - accuracy: 1.0000\n", + "1/1 [==============================] - 0s 13ms/step - loss: 0.6069 - accuracy: 0.7667\n", + "1/1 [==============================] - 0s 13ms/step - loss: 0.7855 - accuracy: 0.9000\n", + "1/1 [==============================] - 0s 14ms/step - loss: 0.7858 - accuracy: 0.9000\n", + "1/1 [==============================] - 0s 13ms/step - loss: 0.6576 - accuracy: 0.9333\n" + ] + } + ], + "source": [ + "accs_pso=[]\n", + "for train, test in kfold.split(inputs, targets):\n", + " options = {'c1': 0.4, 'c2': 0.4, 'w': 0.6}\n", + " optimizer = GlobalBestPSO(n_particles=25, dimensions=35,\n", + " options=options, bounds=bounds)\n", + " cost, pos = optimizer.optimize(evaluate_nn, 20, X_train=inputs[train], Y_train=targets[train],shape=shape, verbose=0)\n", + " model.set_weights(set_shape(pos,shape))\n", + " score = model.evaluate(inputs[test], targets[test])\n", + " accs_pso.append(score[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy 0.96 +/- 0.04\n" + ] + } + ], + "source": [ + "accs_bp\n", + "print(\"Accuracy {:.2f} +/- {:.2f}\".format(np.average(accs_bp),np.std(accs_bp)))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy 0.90 +/- 0.08\n" + ] + } + ], + "source": [ + "accs_pso\n", + "print(\"Accuracy {:.2f} +/- {:.2f}\".format(np.average(accs_pso),np.std(accs_pso)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pso", + "language": "python", + "name": "pso" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyswarms/pso.gif b/pyswarms/pso.gif new file mode 100644 index 0000000000000000000000000000000000000000..bf9a271d79aa2b6b7da5da6c0e60c367b8d20ad0 GIT binary patch literal 4218 zcmeH``9BkmsTbidDu+laPE^mK7nBjQrq6H@yt!($(BR?L$D!uxch8_nXJo8f zYQ`&IB-OjaEwz)?4i(nL)^zGL&8<0TahOhIr1^Y4%k%yDd8~pfC 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zEg8n^tOtrId0=iCia}C!rlrbEP0NJ{x#o%)T#inYGdj2Ci@?&-lkOqYo1qVIp*-l^ zCB4d(HyI}!6*s1Yz z|9Lfa`dT_h-Mho?UtojnGbs}HLs0*{N#BC7M(ylqcJlcmi7 z+!QWzXY9@Fn%z0M!?W@`7%{ZA0cKJTS7DI88pUx^TPjU0H=1): + n_nodes = np.prod(shape)+index + else: + n_nodes=shape[0]+index + tmp = np.array(weights[index:n_nodes]).reshape(shape) + new_weights.append(tmp) + index=n_nodes + return new_weights + +def evaluate_nn(W, shape,X_train=X_train, Y_train=Y_train): + results = [] + for weights in W: + model.set_weights(set_shape(weights,shape)) + score = model.evaluate(X_train, Y_train, verbose=0) + results.append(1-score[1]) + return results + +shape = get_shape(model) +x_max = 1.0 * np.ones(83) +x_min = -1.0 * x_max +bounds = (x_min, x_max) +options = {'c1': 0.4, 'c2': 0.8, 'w': 0.4} +optimizer = GlobalBestPSO(n_particles=25, dimensions=83, + options=options, bounds=bounds) \ No newline at end of file diff --git a/pyswarms/report.log b/pyswarms/report.log new file mode 100644 index 0000000..c782ce2 --- /dev/null +++ b/pyswarms/report.log @@ -0,0 +1,11 @@ +2023-05-28 17:29:35,386 - pyswarms.single.global_best - INFO - Optimize for 20 iters with {'c1': 0.4, 'c2': 0.6, 'w': 0.4} +2023-05-28 17:30:07,637 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.013333320617675781, best pos: [ 0.17027965 0.17696722 -0.07395054 0.31544984 0.17052408 -0.37810479 + 0.24267479 0.16931148 0.65606942 -0.24207116 -0.66562722 0.02191478 + 0.5870387 0.78966943 -0.4457816 0.0907434 -0.1808341 0.29282655 + 0.61472003 0.90660508 0.16469465 -0.55057763 0.54702005 -0.22636745 + 0.01125538 0.62431828 0.02128613 -0.26723577 -0.43527016 0.51223244 + 0.76388399 -0.02073011 0.15949622 0.45878514 0.01787211] +2023-05-28 17:30:08,140 - matplotlib.animation - WARNING - MovieWriter pillowwritter unavailable; using Pillow instead. +2023-05-28 17:30:08,141 - matplotlib.animation - INFO - Animation.save using +2023-05-28 17:31:01,885 - tensorflow - WARNING - 5 out of the last 3010 calls to .test_function at 0x7fee6622df70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. +2023-05-28 17:31:08,794 - tensorflow - WARNING - 6 out of the last 3011 calls to .test_function at 0x7fee66257b80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. diff --git a/readme.md b/readme.md index f10c78e..f9ce348 100644 --- a/readme.md +++ b/readme.md @@ -30,6 +30,7 @@ pso_bp.py # 오차역전파 함수를 최적화하는 PSO 알고리즘 구현 - pso_tuning.py # pso 알고리즘의 하이퍼 파라미터를 자동으로 튜닝하는 파일 xor.ipynb # xor 문제를 pso 알고리즘으로 풀이 +iris.ipynb # iris 문제를 pso 알고리즘으로 풀이 mnist.ipynb # mnist 문제를 pso 알고리즘으로 풀이 mnist.py # mnist 문제를 pso 알고리즘으로 풀이 - shell 실행용 ``` @@ -61,3 +62,7 @@ pso 알고리즘을 이용하여 오차역전파 함수를 최적화 하는 방 >
> > > pso 와 random forest 방식이 매우 유사하다고 생각하여 학습할 때 뿐만 아니라 예측 할 때도 이러한 방식으로 사용할 수 있을 것 같습니다 + + +이곳의 코드를 참고하여 좀더 효율적인 코드로 수정하였습니다 +> https://github.com/mike-holcomb/PSOkeras \ No newline at end of file diff --git a/readme.png b/readme.png deleted file mode 100644 index 45685b11c2c097b8ec91120a21b6efd9db405834..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 228442 zcmd43c{rA9`!;-Ql#(GN8q7(C%#^4Q8B(GsLo!5(%w()6V^ZddgfdT=iOfQhIYfvu z&$IOI*ILiBy}v)czrOAJw)cIu^=zwEx$o;buk$>PeL60m3+ELmDVQioBogI0MLBg6 zX)_Cnv{jLO8-7P;dxxcD<>*UZ)r8$+2VKBZITKne~yrzUtZO z9aDjBzc%cTuZ`H&x%7Ui8I<~6GBsA1ZZo;y+SgHT=r2fphMd)t`i%EBQ&C&8e||~M z`u7AkiTF+Yr|GV(EdTzBIv|GhpI^NZr1SXC&xt(R^xsdYzCil-v&g?PQk?qFM={C% z_e13j|Brm=aY|j%Y@epKw$J#uImx4-Kr%ZghdE#FpIeEG`O5tCESsPp&8GV#{Nv^2 zMX9?#$8r8QO-f42bz@_%;Naj#{O4n{jXu%-9vd@kdVQLLFFGV-tE;Ok{+s>Ck;EwF z!;>3-*6!Ncdd9}GXJuuHWHoknYK`}m9vT@L>B;u=+@f{uTI{8F=b4UFxNFyBu$r@=rw^@^TeR9Ej)ZX0&}7cX8gH%6Vj_HN-Y7Z=aE 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+ " 0 1 2 3 4 5 6 \n", + "0 1.065477 0.341667 1.620457 0.333333 0.973868 0.733333 1.119378 \\\n", + "1 2.018898 0.350000 1.063357 0.333333 5.985742 0.333333 1.492205 \n", + "2 3.812844 0.441667 0.973291 0.391667 0.982345 0.375000 1.206667 \n", + "3 0.992694 0.666667 0.919116 0.583333 1.219213 0.333333 0.904292 \n", + "4 1.111336 0.333333 0.874328 0.433333 1.070013 0.308333 0.974060 \n", + "\n", + " 7 8 9 ... 190 191 192 193 \n", + "0 0.333333 1.191749 0.375000 ... 1.764640 0.333333 1.150359 0.333333 \\\n", + "1 0.408333 17.772097 0.333333 ... 1.721332 0.333333 1.078496 0.383333 \n", + "2 0.525000 1.002070 0.391667 ... 1.320217 0.333333 0.978259 0.450000 \n", + "3 0.475000 2.042726 0.000000 ... 1.152177 0.333333 2.090459 0.333333 \n", + "4 0.633333 1.245026 0.333333 ... 0.967579 0.825000 2.405662 0.333333 \n", + "\n", + " 194 195 196 197 198 199 \n", + "0 1.353020 0.333333 1.798916 0.000000 0.980328 0.441667 \n", + "1 1.102060 0.333333 0.856502 0.333333 0.674042 0.375000 \n", + "2 1.075823 0.333333 1.588661 0.650000 0.701815 0.483333 \n", + "3 0.885551 0.666667 2.373989 0.583333 0.842598 0.666667 \n", + "4 0.876492 0.666667 0.979572 0.516667 1.128002 0.333333 \n", + "\n", + "[5 rows x 200 columns]\n", + "100\n", + "100\n" + ] + }, + { + "data": { + "image/png": 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VVW644Qay2SxXXHEFP/vZz8a669PG0YKRcanAWpgx4wqAr3Rw+UnOjBw6RkR200iSJEnjYczBiBDimOt4vV7uvPNO7rzzzrHu7oSyTJun7trGhJmlzLtwwqgfd8IzIwP5mTThemesSMFJLnxmHtItI7tpJEmSpPEgL5Q3RGdTlD3ru1j/2P7jetyRqq/COAUjhw5eLSheLO/YwYjZ3U3vr3+NNTDwmptxaLeMnE0jSZIkjQcZjAyRijlBRTpujCrjUzCazIgQ4rVXYR1p8CoMyYwce8xI329/S9d/f4+2L9722trAYPChVVQAsuiZJEmSND5kMDJEOu4EFZZpY2RHHziMJhiBMZSEP1JmJFTn/Ez3Qy7F0RhtTi2SxDPPkHj++dfUjEI3jWfKFEBmRiRJkqTxIYORIdKJwXoh6bhxlDWHG20w8pq7aoqZkcbhy71hcJc4vx9j3MjQ8R6d/3k7It/m41EIPjzTpjp/DwwgxnrNHUmSJOlNTwYjQ2TigyfodGL0J+ujjRlRVbVYe+Q1ByPFUvCHZEYKhc/gmBfMs3p7i7/nmpro+/0fjrsZZn7MiHuykxlBCKz8tYgkSZIk6bWSwcgQQzMjmXHKjMAYB7Ha1mC590PHjMCoL5hXmIZb+oEPANDzs59h9vQcV1MKs2f0ykrUcDi/THbVSJIkSWMjg5EhhmZDjiczkk6nAWcK80jGFIzEO8A2QdUhOEL5/NCxa40I2y4WLCv/6EfwzpuHnUjQ9YMfHFdTCtvQykrRS516J7LWiCRJkjRWMhgZYug4keMZM5JIJAAoKSkZ8f4xBSOFwauhOlC1w+8vTO89SmbEikbBtp22lJVR/eUvOZv+6/2kN28edVMKWRC9rAytrCy/TM6okSRJksZGBiNDDBvAmhi/YGRMVViPNHi1YBSFzwpBhBoOo7hc+BctInztOwDo/PZ3RjWNWZhmcXyIVlaGVuZkRmRJeEmSJGmsZDCSJ2xBZuiYkePopkkmkwAEAoER7x9bZuTwwavDgodRlIQ384NX9Xw2A6Dy1n8DVSW9aRNmV/cxm1EslqYoaOEweqmzLdlNI0mSJI2VDEbysikTYQ+e5EebGRFCnNhumkMKnpn9/ey56GLav/IVZ3loFN00hWJlQ4IRV3UVemWls82uzmM2oxB0aJEIiqYNdtP0ymBEkiRJGhsZjOQdOmB1tGNGMplMsZjZsYKR11T07JCCZ+lXXsHs6iL6t4cRhjGYGcnGIBMbcRPmkLEew9pVVeXc33nsYKQwNkTLD1zV8900puymkSRJksZIBiN5hwYfo+2mKXTRuN3uEeuMwFgzI4WL5DnBSK7ZCU5ENkt2715wB8AbcdY5QnakkL3Qyg8JRqqdYMTo6jpmMwpjQwpjReQAVkmSJGm8yGAkr5AZ8QWdgGK0mZFjddHAGIIRIQaDkdKJABgtzYNtLsyEOcb0XrPv8DEjAK6qauf+zmMHI8XsSn6syGAwIjMjkiRJ0tjIYCSvEHyU1jiDUI2shWkcu1vlhAYjiU4wM6Cog5mRlsFKq5ktW51fwke/YF6xi6WsfHi7Xks3TT4IKQQ28mJ5kiRJ0ljJYCSv0C0TrvKhagowuuxIoZvmhAQj/Qecn6F60JyMjdE8mBnJHJoZibWPuBmrOJumdHi7qvOZkVF10wwWPHN+lhWXi3wNE0mSJEl6LWQwklcIPNyRTZRUOV0PmVHMqClkRo40rRfGEIwM5IORfBeNsCxy+avvAmR278bOZo9Za8TsHzkz4iqOGRnFbJr+Q7pp8gNZsSzs2MgDZyVJkiRpNGQwkpeO53CHWskGvkzlojuLy4CjFgU7od00/fudn5H8eJH2DjAMcLnQIhEwDLI7dzrVWWHwGjaHKGZGDh3AWuimGU2dkUO6aVS3GzX/nE05iFWSJEkaAxmM5KUTBp6QczLX/a2ATTphcPeWuznvj+exrXfbiI87scHIAYSAXIlTS6QweNU9YQLe+fOddm/ZctRgRJhmsWCZdujU3nw3jR2LYeevr3Mkg6XgB7t6Brtq5CBWSZIk6bWTwUheOm7gCjgnVUU10b0x2qLt/HTjT0kYCZ47+NyIjztW9VV47eXgrb4D3N8yl5/9ZhV9ba3kWpxpva7GBnzz5wGQ2Xz0YGRY5dRIZNh9akkJis/ntO0Yg1gHu3oGAxp5sTxJkiRpPMhgJC+dyOHy9xb/1gO9/KX3D2StLADtyZEHh57IzMjTG+PsT5ZhWTatO7di5IMRd0Mj3nn5YGTL5sFgJBuFbHzYNsxCjZHSUhRt+IX2FEXBVXXsWiNDr/pbHCuCrDUiSZIkjQ8ZjOCMCckkDHT/4Dd8I9zEC7mnin+3Jg4fHCqEOK7ZNMdTgXXj4w+zqTNY/DvW1VkseOZubMA71wlGsnv30dPSzqr+6Ri2etiMGitfY0Q7ZCZNsW3Vx641YsdikG/78GBEXixPkiRJGrsxByPPPfcc11xzDXV1dSiKwoMPPjjsfiEEX/3qV6mtrcXn87Fs2TJ279491t2Oq1zaxLYEWqCPe/gAa1lCd80abCwqNacbo71/32GPG1oKfjxn07RsfZWnf/srAErdzliOaHcXufyYEVdDg3NtmaoqDAX++t2v81JHDduiVYfNqBksBT98Jk2xbcVBrEcORgoDVNWSElS3e/CxhVojsptGkiRJGoMxByPJZJKFCxdy5513jnj/f/3Xf/HjH/+YX/ziF6xZs4ZAIMAVV1xBJpMZ667HTWFab1Oggr8r1/E7PoIS6gDgSxmna6M904sthtfTKHTReDyeI5aCh+MLRgY6O/jbHbdj2zazQl2cN8XpJop2dWAUMiMNTgE0z7x5bJlQQSLuTK3ty/ohfkhmpPfwi+QNa1t1IRg58piRwVLww7ehlcpuGkmSJGns9LFu4KqrruKqq64a8T4hBD/84Q/5j//4D6699loAfve731FdXc2DDz7Ie9/73rHuflyk4zlUV4o2l5M96FMqCKs+ZiXmcnH/42j1lRiKTU+6hyp/VfFxoxkvAoPBSGLTq/SmM5Td+H6UIRmGAiOT4cH/+iaZRJzqmlIuD79Ib+UlsCNLrLMDO78/Vz4Yaa+M0N472JUzYHgPz4z0j3yRvAJXvpvGOEo3TWG8iF46vKun2E0jMyOSJEnSGJzQMSNNTU10dHSwbNmy4rJwOMw555zDqlWrjvi4bDZLLBYbdjuR0gkDl7+PTmqKyyy9gkvazkQ3M1SbTldMW6INIQTfe2Inv31p/+BMGp+XPevWYB0h81EIRnID/XT9f/8f+669jsTzzx+23u6XX6L3YDOBSCnXXtKIS7UJ1TqBR2JgAEtxulVUr5doVwdr9m4HoMJwMjZRw3vYjJojXSSv2LZRddOMnBk5npLwwrZHdUE+SZIk6c3nhAYjHR1OV0d1/tt3QXV1dfG+kdx+++2Ew+HirSGfCThR0vEcur+XTmoHl7nLmZJ0TrK1+SCjLX6Qza1Rfvr0Hr7x8Fb6B5wgyWrdz0P//S1W/eWPI26/OIBV01DcbnJNTbT8y7/S8vFPkDtwoLhexz5nLM2s8y8kmHOCCl/NFFweLyDIuFy4GhuwLYtHf/J9DCNHaSLNnL3ONWmiOS8ieuiYkZEvkldsW/FieUfppukbXgq+YLCb5tiZka7vfZ89F15E8qWXjrnuibDusf388ZtrSMVGdzVmSZIk6eQ5LWfT3HbbbUSj0eKtJT+l9URJJ5waIx1DgpFutZIy3246NI2s4lyrpq1nG6v2Oid3W0BLt3OStqLOz63PrRj5Oi3RqLOepjH1sUcpu/lm0HUSTz/N/vf8UzGz0LlvLwBVk6cVS8ErpZMIVTrZi5Rbx13fwJoH/0zbru24fX4WGyr+nIECmEIj2XPobJqRS8EXDM2MHKnSrHWErh59SDfN0arUAqRWrwYgueblI64jhCDRnznmtl6Lrc+30teWpHlb77FXliRJkk6qExqM1NQ43R6dh3zr7uzsLN43Eo/HQygUGnY7kTJxI58ZGWxTFzWEgk18vaKMLV4PAG39u1m9b/Bk1tk7AICZH0Ca6O2hdefhlVqN7U53ivD7cU2YQPUXv8CUhx5Er63FikZJb9iAbVt073dm7FRPnjZ4kbzSiYTz2Yu0WydTWV7MwCz7yMcpmzMPVUDA7bRxoHd4luJIF8krcFVVOm0zjMECaYcozKYpZEIKCt02wjCw811WIxG2TbapCYBcPuAayaYVLfz2tpfYsWrkmi6vVS5jkuhzBgL3th65nZIkSdKpcUKDkcmTJ1NTU8OKFSuKy2KxGGvWrGHp0qUnctfHJZ3IkQpmySme4rJOarCD7az2eYvLDsZaWbt/cHxENBYH2yYbixaX7Xjx2cO2b7zqXF1X5KudAnimTsW/eDEA2V276W9rw8hm0D0eSsvDkOpxViydRKjSCUZSbhf9Lg1h29RMm8Hst7y1WInVLwptyoExOFOpON6jfOTMiOJ2F4OKI3XVFLphtEMGsKo+H4rfP2ydkZgdHYh8ufnsvqYjrte6sz//c+CI67wWA52p4u99bYlx3bYkSZI0dmMORhKJBBs3bmTjxo2AM2h148aNNDc3oygKt9xyC9/+9rf529/+xubNm/ngBz9IXV0d11133Vh3PW7SCYP+kuHVSTupYVfYxsp30QDs7fOQyA4OUk2nUqi5DDDYrbBz9YvDBrIKyyK3aRMA1iHTfz3TpgGQ3bOHrqY9AFRNnIIac8aA4I2ANzwsMxITzrarJk1xVskXP/PFnG/8A4a3OL1X5HLYcaci65HGjMCxB7EOzsg5PLtSLAnfe+Tuj6EBSO7AAYQx8tWQ+9qTw36Ol/6OocGIzIxIkiSdbsYcjKxbt45FixaxaNEiAG699VYWLVrEV7/6VQA+//nP86lPfYp//dd/5eyzzyaRSPD444/j9XqPttmTKh3P0ZvPgJSmBgDooZL9Af+w9dqTEwBoLMsvNzP5YASqp0zHH46Qicdo3ryx+JjM5s0o+dlAUTPJyv0ri/d5pg8GI535YKR6yrTBq/WWOlfrDUacE37a7WIg4QQX5fXOfd55c52fA052JpobnFFTnOWiaahH6eoq1BoxjpgZOfy6NAWDF8s78oyaYV0zplm8xs5QRtYi1uMcy/72JMIev3Ej/R2DAUiiP0s2NXIwJEmSJJ0aYw5GLr74YoQQh93uvvtuwLn+yTe/+U06OjrIZDI89dRTzJgxY6y7HVfpRIpudxiAULwdl21iKxp93goApgaca79kU1MBeP85jWgquIWBmnVOoBUNE5lx7gUAbB/SVZN48UU0O18G3oZfvvSb4n2FzEhu374hg1enFgevEnECjkB+TGza46K3o624PwCtpAT35Mn4s84JdsDwFYORYvdKWSmKeuSX2lV15JLwQogh3TQjBSPHrjWS3Te8em1u3+HVbIcGDKZhE+sdv6J4QzMjILMjkiRJp5vTcjbNySSEwLQ7ijNpXGY/laZzYjf0KmaYYa6ecg1CaFipSQBcNKOSmeUeNEUUMyNlE+qZdf5FAOxZuxojH6QkX3gR1XKiCU1oNGX2FGeLuOrrUbxe7FyOzn1DMyODg1cBvMn8eAtdI9rtZC8qGhqLz8E7fx7+nNNmJzPiTO8tXCRPHyGIGOpo3TR2MoXIOdNhR+6mKZSEP0pmZK8TfCj5bFh27+HByKFdM/3j2FVT2JbucbriemUwIkmSdFp50wcjRsZC8wzOpPF5uim3nRN7Rq+h1K7kj3vux86WgXATcFvMrA4ys8IZ/6HmnFkaZRMaqJsxi1BlNUYmzb5X1mHFYqRffRUtf/0aBYWMmqIt7mQuFFXFM3UqKbfLGbzqclM+oWEwM1I6ydlHdzd64SJ7QuALhfGHI8Xn4Js3D3/OGUuSstwYvc6Yk+JF8o5Q8KygWBJ+hG4aa8AJMhSvF9XvP+z+wSv3HjszErjgfGDkGTWHZivGa9yIZdlEu5xgbtL8fIXdVjmIVZIk6XTypg9G0okcemCw4Jl33yZ6Y68A0K/U0KIm6E53o3p60XxN1AQ7UFWFSSENhEDNOSe6srp6FEVh1nlvAWDHi8+QXL0aLItcaHAmiyY01u7dWPzbM20aUb8zi6dy4mRUTRvMjEQmAZBrbsGXGxwUW1E/mBUB8M6bj8uyceUzMAMdTjByrIvkFRRLwncfnhkZ2tUzEv0YV+61BgaK04uDlzqVeEfKjBSyF4GIcyzGqysl1p3GtgW6W2XiXOc4yMyIJEnS6UUGI3GDTDBJRvGBsHnLq0Fm7HbGGLRTQ4fmnKAVxcbXcDeWfw0ANX4FxTRQbBtV04szXmZdcDEATRvW0f/ccwDsK40U96cKlU2tm4t/e6ZPI+ZzrlNTNXkqCHHYANZcS3OxGwagPD9epMA7exZoGr6s050S7XGmBR/rInkFxW6aEcaMFAOaI3T1aMfopinMpNFraorTkHP79h1W2KyQCZl6ZuWwv7t/eiedt38XMcorHh+qMF6ktCZA+QTnGkJ9bckTUlhNkiRJem1kMJIw6M9PNAmkY/hL/pnazGwA2kQNtmJT6inFSk1C0bL0BdbRFG2i1G2jZp2sSLi6hmTUQAhBZeMkyusbsUyTPa+sBWDrRBcWTjeLJjR29O8o7t89bRpRn5MNqJ4yDVK9YOS/uYedMvjGoZmRhuGZEdXnwzNtGoH8OgP9TjdEsRT8MbtpnEDK6u0tjg8pONpMGmf50Qew5pqcLIhnyhTcjY2gadip1LAuoaEzaaad6QRG/R1JMrv30PPTn9L329/S8e1vv6YAojAwtrTGT2mNH0WBTNKQZeElSZJOIzIYiefoK3HGf5TGMyiKRlnaOcH2KtUIFCYG5pBquRkrU4NQbD76xEfpSh0oDl7NqqX835dXse0FZyxIYSBrCwa2orFuahJbcbpQVKFywBgcM+GeOhiMVDVMHOyiCdaCy4uwLIyDB4tjQuDwzAg4U3x9+Rk10UQOLOOYgUSBFolAvgaKmc+qFBS6X7Qh2Z2hBi+WN3IwUuiScU+ZguJ2OwEJkN07eAwKAYMv6KJ6cghVVzBzNj1//VtxnYF7/0Tf3b896vMYSX/7YGZEd2uEq5xxL32yEqskSdJpQwYj8Rw9XqcyakXUKXAWSdmoto2luLG1CGp2Ithecn3nAdCV7uKnnT8tBiMdcUFm4BdsfPJFYDAY6S3x0V06mbbSdiwlnxmxdeLaAN3JbpLRLG29OUxdQ7UFQRsY2O80LD941ezsRBgG7iF1NyrqDw9GfPPnE8h35QzkvJDoPOZF8goUVcVV6XSPHFpr5JjdNMUBrCN30xSm8XqmOkXa3PmfuSGF0ApdMqU1AVRNpbQ6AEKQeOxRAALnOdV6u/7rv4g/9dRRn8uhhmZGAMrqAgD0ykqskiRJp403fTCSSmTpdkUAqEw6QYkqIJxwAg1Lr6a72+nG0HyDV8RNakkswylA1mEBIkXXvhfIZUwi1TVENBcoCvuqS7FUA6E6wUQk65z01zVt4sE7NvDYL/ejaBWUZLKYTfuHDF51Ao74fieDoBYGkCoK3pKSw56Hd+68YldO1HAKnw29SF5yoJ8X//x70vnr6Byq0FVjdnUPW37Mbpp8kCIyGexU6rD7CzNp3JOdIMST/5kdMqOmMFi1ECiU1QUIxg8gOltRfD7qf/pTIu97LwhB679/jvTmLSO25VBCiGFjRgDK8/uQtUYkSZJOH2/6YCSb6qJLcU7EpYnB0u+hlHNit/VqdrZEAPD7hk9JLWRG2tzO/bbRTMv2LuxslopOJ6PQHczX6NB1AKpsZ9bO5g376GvdjZnZiupeSDidI7t792GDV//w1PcASIfy02qFIJc+/KTvmTmDQD57Es15sQdahl0k77nf/4bVf72XF//0+xGPw+Ag1uGZkWPNplEDfhS3MwD30EGsdjaLcdCZ2XNYZmTIjJrCTJqy2nwwUuunpnMdAMFLL0X1+6n58pcJvOUtiEyGlk98HKOtbcT2DJUcyGJkLRRVQax8kOYPf5jSoJOh6pXTeyVJkk4bb/pgJJNrLU7rLU3YRDQnIHEbTgbBVCZh2R48bo2QZwCAYKYMl6HgyY+BPOBvyG/NZPsL60m+8AJVfU55dsVMoloUj3TO45wEU7tscokHMVJPoKpBghmnLPzQ6qtbe7YSb9oNQIdrcDZN9JApuC2xFn6z83eEJk9GsQU2KrEDu4uZCiUSYV9+MO3eV14ecSBosdZI1yHdNPky70fq6lEUZUhJ+OHjRnL7D4BtowaDaBVONVvPVKeK7dCqrH2HBCOl1T6qupxgJPT2q5396DoTfnAHnhkzsLp76Pr+HSO2Z6jCeJFImU7PD39I8qVV+DY+VdzneJaclyRJkl67N30w0m/3klLyJ8GkRa1LIaQCVv76LqqTocgiSGpeSlO1vHvjF6mIOY9J6m5irggpzfn74LaNDPz9ccLpLLqloVmCmj4vHb4Af1p8CXtLqtAsF6H2LAinYJpl7MIome8EI0Oqr/5222+pHnD+7LWzxTZHDwkYvvrSV/nRKz/iQJ2OL38Ruv78mAzF5aL9YDOZpBMEJXp76Np/eJ2PYq2RIVVYhRCY3U63TaE7JpPI8cfvr2Pbmvbieno+0Mjt3z9sm0Nn0ij5Cw66J092nnNPD1Y0OmwmTaGbJtC+DY8Rx3AFCCw9r7g9raSE2m9/C4D4ihXYyaN3tfTlx4vUZXZhJ5znn3vuqeIA2fEsOS9JkiS9dm/6YKTd5ZywQkkLlwVerYdyXSGjOOXZLd0Z4yFyNqZ1GUua34aOyqQ+Z3nM73TnGNOda9ukBnYSX7kSBfAFnJkjDV0+Xq2ZR38gRE/pO5jYPw8lvafYBju3m97ys8keOIDocy4i1+728eS+J5jW5nx7t6zBlyrWPRiMtCZaWZfv0ngx1Ik/f1XhvnZnHa2sjH2vrBn2nPeuG/43jFxrJPnCi5jt7SgeD9qkSfxtUxtf+OYL9O2O8ejd20gMOAFS4Hynsmr0bw8P22Zhxox7ypTiMq2kpDg+Jbtv37CZNL6g091jvehkLzorF5GIW8O26Z0/H9fERkQmQ3zl04c9j6EGCuNFWtYOtmnbNqr9zlifPjmIVZIk6bTwpg9GuvLBRFnCRhWC3T0r6Us9gz/udI8ITwkeO4kCRM23UptYiKllqIk6xUmi4RgoWXr9DYCCsAdI2zky7gj+pc4VdSujlbSW1QOQ8IeoTr4V29w/pBUGcU+SaGAi2SigurindSWLdptURSEe9OCy9OLaQzMjj+x9pPj7mkhvsTjaQJ9zklfLS9mTDz6mnHk2APteefmw46AXL5bnbFsIQfdPf0JGc/HUOz/Bsv/dwG1/2MDkmBMc+YTCH+9Yj2XaRN55HQDJF18cNhunMC6kMF6kwFOcUbPvsC4aO5sl8eSTzmtTtfiwsvCKohC++u0AxB55hKPp70iiWjncO53nq9c53XF1fU6F3RNZidXo7JKF1SRJkkbpTR2MGFmL3nwp9tKEhdfaTk+mmc74eq5a5ZxIhEvnqr4n8ZspzD6L1TO8NJduxu/UOyMazKEHt9HUeTa6LwJAV9BPT/UirMVeTNXmQMOZ2EOumttSWQEIVN1dXGZlt9Na9xZyMZ1kpJ6/7L6ft611apP0XXSW09786oXMiBCCR/Y5J+SgO8jBCvDlK5X2DzhBSSoUItrZgaaqTFuzEVDo3LeHeN/weiJ6lZPpKVwsL/nCC+za28HyS/6N72Um0NKX5kLTgwsFG+fYdHWnePaPO3FNnIjvrLPAtok+NFgbJNvkdBUNzYw4fw+OGynMainNByOJZ5/FTiQwg+UMhKeOOOulMI4k8eKLxTEtI+nrSFHetw0yafS6Wio/+SnnWO1eBUKcsGvUDPz1r+y56CK6f/ijE7J9SZKkN5o3dTCSjufodjvTZEvjJrHkCwB0BfykPGlKEs4gVM2n8Za+F1H7sqyZrBOKbUfNX5U3GjBwhTfSrdko6iwAukN+lLPPZlPnOtrKM7w6ywkmJnc741C2T6rEVDX88XixLbZ5gM7y6USjZdxfVkVFS5y5zSA0jX3V8wEY8DndIoXMyJaeLeyP7cerefnC2V/AVhUSASeA6TWD9Ien0e5yxmqUDcTRt+2gND9oc9/6wa4LGBwzYqdSWIkEf//f+/nsRZ+iLVBBTcjLN5bNZH7aebskqzzYCCJCZduLbWx+5iCR69/ptO3++xFCIGybXD4Y8RwWjDjjRnJ79x02kyb2d6e2iL34YlDUES+Y55kyBc+c2WCaxJ94YsTXNpM0SMdyVHU5WZDQlVcRvGwZisuF2tlMINl2QjIjdjJJ1x0/AKDvN7/BaG09xiMkSZKkN3UwkorlaFOci6dF+vdi2gmSLj+PzHfzwMVtuEwn/TEQLmdGcg/V2S6s3iyZwNTi1XqjJQZaYDd97ji2Og2A3hI/Ww++wNqml2irm01PeQ2aaXLhro0EUwYZr5e9E2eTcjtVT0vSWUBgmnvZrVzEH1w53rbOCSqebjiLNTs7AOjPTxOO5WfTPLzPGaNxSeMlXDn5SkpcJRwsddqVcHnYuPCT7OsdAKA6nkbxeqnsdKb7HtpVo/r9qMEgAvjZ//yd22ouIeXysXhCkL9/+gIaO0wKvQ7Xv2sGBzzOH3FF8MJ9e4hNWYLi85Hbv5/0ho0Ybe2ITAbF5cJVXz9sX56hmZH2wRojViJB4mlnHIj/8quc53yEq/cWumqiR+iq6e9IoVpZKvqc6wD9QpnE9b97Fe28C5zj0bWegY4UlmmP+PiCzLZtxxVQ9P72t8Up1cIw6P7JT0f9WEmSpDerN3Uwkoz30qs44whCPasBWBVZAiXOINKKfN2R/pBTY+O8vtVo7SkOVlWhIBAoqJaCothM924lqpehKAFsVSFHgGjAoK/mGgCmN22jJGExd4+TLdg68yxsTSWiQ2OvM43Yym2nKXgJiZ5ezt8m2FY2kR/Mv54yw5ky28ZCALKpJPFYP483PQ7ANVOvwaN5WDZxGQeq8mNLRAZLxEm6nG6b7vP/g86r/43yuNN9s3/TBl5+eCc713QMHpCqGn58xrv4flcJtqLydnc/n7s+wIqdj7L9JSer4w+7mT6vkuB855j4URG24B//14TvYueqvNEH7ieXL2rmnjQRRR8c7wKDY0aMgwdJdDrZobLaALFHH0XkcrinTqX8XOe5HmkKbujqt4GikF63fsSaI/0dScp7t2LbFk/Vn8ldPT42tQxwf/kCAKq712NbNgNdh9dsARC5HB3/+Z80XX8D+95xLZldu0Zcbyizv5++X//GeT4f+pBzLB56aFSPlSRJejN7UwcjrT17yepO90A42kGvu5Tm0nI0dy8IhZoBLwB9k5yMQUOmlWkdO1jgW0V4UhxV93BWbDbv6bmCD4pqJigC1eVkR3J6irrYXPZMcGapzN+5Hm+/weztzwDQ1DCFpDfAgG5RGtZRbIGwukjpcM36GTw0+SI+d8HHKdfTVOWc8R0d9pkkNadK7PJfPUJvwqTcW845NeeQzXbytslvo9K+GRSnQJqVWQ+AotUQjfrZ0lvHK2ffjqKGsS2T1Q88y1N3bWPr8843/1/NeQslM32cpbXw0R2P8a83TuTjK/+FZx/aisgnEBa8tR5FVXjnFVPpUW10Ae5SN9mUyQ7NCSBijz5GZutWYLDy6lBaRQVqMAi2jS/dhS/oQmndR9d3/z/ntbj2WiJV/uIU3Hjf4VNwXTU1+Bcvzu/v0WH3xdMG339qF/eV+nnv277O9xe/H/JTi39p1mF7vPjSPQTjzSNeoyZ38CD7b/xn+n/3f4DT9XLwYx8/7Lo9h+r9xS+xk0k8c2ZT9bl/J3j55SCEHDsiSZJ0DG/aYESYJnuf/yMIQUkyhts06J9TxvcuvJ1/r84QsEqZGXGmzFoVYaoW9BCeEuPWt/6CxdNXM/mKgyxbOocvRD/Oh7qv5cLUAi4OeQiVONdRUXIxSo33k3WrhBIpGlv3oUZ3UNHfTE1XK7amsXPWMpSMiw2VfsqTzjd0K7sdod/MC/Mu4G3enbxN24zbziGAZPk+4i4nQGrdP0By378xifeyefPHeOHF80iu/Qvl2TmgOdOMDWMbAN7gDNR8MTdF1VB1Z8yG1+9UR33uj7v4yU//RkkgSYmSY76rg8pFFXxhw3/iTYWY1XUOADYWTdUbEUIwvz5MR6WT8cgZNppbZU+8BlFRi51M0vd/TqVX95CZNIXZJYqiFMeRBFIdVAUztPy/j2GnUvjPPZfym2/KX6Mmf1G7I3TVhN7udNX0PfQIbbsHnDbaNm/78fOsSCdZWzmRlMtHxK1w/aIJqArYqsadi94FQHXXusOuURN/6imarr+BzObNqOEwdf/9X7gnTsRoa6Nl+XLszMi1SYy2NvrvuQeAqs/eiqKqVN7yGVBVEitXknplw4iPkyRJkt7EwYhlWiTLW7l94LNctvFvUCWYpq/ib8+U8vy2IO+ubGfSBKcbxC1yVC7uYfJlrfiCWVTDyU50z/kDnfUrWevdw15PC15F5cJQkJLgMjxGil0NQQDmNyVQhQDhBBxzdzonpq0zzmBBzaep1BbRnu9esYztlChe3mEoNIY6idY5jzG8fi6zNM6unM07Gz/DHb4ZfNQOs2fDRL7w+ALaY3U0vXAmnWGNvROcAmWqcGp0mPYkbEsQyZ/cNd0p5Jbu30wgEsW2BWx1o5pe7L4sumnQLmDBzjO4ovkDqGgA7C/bwjde/Qq3rPw4HdGtLHnrBLIISJjMOa8WFIX9YSdbUSgj75kyhVza5O93buKXn36WtX9vQgiBO1+JNRTbT+OKOzA7O3FPm0r9j39ULC9fmGFzpOvIBC+7DKHpmLt38sQ3/85jv3iVf/rZKlr60yhC8K7dT/PDLfey/mtXcsc/ncG/Xz4TgMeqF7K9dCJVXa/Q2+J0keUOHKD185/n4Cc/hR2L4Vu4kCkP3E/4mmuo/8XPUcNhMptepe222xD24eNMun96J8Iw8J9zDoELzi8+98gN1wPQdcf35VRfSZKkI1DE6+ATMhaLEQ6HiUajhEKhcdlmLpNixcozcXsNzIzGwedrGNg3uG2BRXJKgvDZQeaGd6EqAsVyU7b/KkJNV7Fr0v24pv0DgP8RH2dzcio/3qQzKxkhjcED8bXcccPloCh86NFOKlp+A8I5qT5Q/3b2vO0cUBWmPdvN2/qzWP5X8DevAVNhylXNhBqddbu3lNK1aiJzGs9ksnYeHls/7LlsxmRPzmJ3hYf7zw5wzoaVnLf+Ged5aGG8wQ+TdSnsqB9gYTyI1bEKf/kKwhMTWIEAmY63kmldTMo0OPOV/6JlnsWe6veQcJ3JuulB0m6Fs5OvMqcswf8O/BoLgU8RXOh3MfD8f7Io68E7oZfSQDV9r3Zx3uqvouSn/6o/+D0rn0hgZwaLlxkBDUXfQFuyhzQulJ40YUVQ+c556CX7WTx1MjMbL+PlB1ez89kgkSnrmHP5NoLBeYSC8wgG5yHMSp7+3Q6C93yHyt7NNE28kt/PupKXfM4YmY/sXsW7tv6V0M0fYcIX/905FkJw5Y+eZ2dHnIr0AHeuvIODU9/FZG8rvs3PgOW0sexDH6Lq1s+iuFzFNidffpnmj3wUDIPyj3+Mqs98pnhfds8e9r3jWrBtJv3pXnwLFw4+144O9l5xJSKbpeGXv6DkoouO+J402trINTfjmTkTvXTkawFJkiS9noz2/P2mDUZs0+AH3/8PpsxaSSjofIuPHWykf+08DHKEZ++ifHoLquYcHm/bWdTufh+5pJcX+zaSsHVql6wjMGsz+8UUHkxcQpOo5/qeGmqybjKqyXNlCSJmircf2IKSOoAmnJPviuAiYhNr6S8rZULnNt5pPoiplrGhby5bfTMIl0SZ4u6mWovRFiinU6vAn/MSyAQQWTcHjSgew0uDVUFI9WJrFh5bcGaHlya3zopwgldLbUxdx5dV0JQgesVBzsusYp54hVp/K5o6/Nu9sBVinQF29M5iq3sB62vPJe0vJWRYqEIQc+sYikVtqp3qZDPeTBdWtIeaTjczYwa2mSOmlGF7SpnWuhtPpo+sfzbRsreiomGpBjlXH95sJaAS92boD+0g6TNJuP10uEs5qJcR1f1gQ1jrI0AvlQkvLiNGf8l6Sm2biKoTyoSZ1DuDoOGlsm0bM9Y+xH1zL+eu6ZcDcH3PLj780q9RbYvyb36L4OJzsdN96EaUfsXmgnvj2ELhos7dfG7DnxBlNrlJBgML/fQ1eMmWTCMcOpNIaBYV4cnUlwWxbYNX/vQ4G/78FN2+CJOsBHNEnLDfIGd0oXh6yMytxz53Joq7DYsuXK4S3K5yzH19sLMTlDDWjAWoEybjKqvDI9zUlk1CXdNE7IFHSK1eXXw9XA0N+ObPxzN/PvrkaVDTgAiXk8n0kckeRFEFmq6g6qAKE5dPRdEEtshhmBni6Sym0LFsF6apYQ4kCGsRJk+ahyscKWafhr0HhEAIUFVlxPtyuR4ymVYGkt3YtsC0FSwbTBv8vlrKwlMIeX0jPv41/R+1DbJZZ4C1ougoqgtV0VFVN6rqRVHetIldSXrdOO2CkTvvvJP//u//pqOjg4ULF/KTn/yEJUuWjOqxJyIY2b59I8s3dbE/EmSy2cQC3zpmKDuoy7bj1dLoer6S6UA1e/Yvpis7gYzbR1JVSeo6vcEInZFK+kKliKN8KGrCZAY7WMgrLGQDdbShY464roFOJ7Vs5EzWsYQ9zDjqtkdSa7WzML2XuX3tNOT24ou04w11obtyw9bLZAL09TSQUIIcrKhgm2cWr7KQpBI8rv2Vi25msp3p7KTS6iacTuJNGuTSJRiGh6Tqp99VSZ+rgm53OW3eStq9FaR073HtB6DETFCZ66My10fESBI204TNDBEjg9cyMFUwVBVTUTAVlazqIqN6yageMqqbjOYio2ukdRdp3flbaIBiI1AA5ySqYaJhDd5sG11YuISFblvowsSlZdG1DLpiomGiYgND/ysp2KhHvQlUBCCEgrDc2IYH23KBraDagFBQbND0HJorjcudQtNzkJ/J5TxewR7xd/LPSEDx2dkotoIwvM4t5wXhzAtz/glQbFTNRFUtFM1EUU0U3UDRTYRCfmtqfv9DbyCEim3mt215QJB/HgJF5NuS/1nInCnYKIo92D7NQHFlUVxZVN1AKEr+eRWO2+Dzs4WGsDXnWVr5320NYWlgq85+hEAVw1qZH8tsoygCVTVRVMu5aaazL6Fj2zq20LBtHYGKLQrPWUUIpbhtZeixVZxX1Xk+zvZRxLDfh3KOmdNuW2gIoTq3Q185Jd92RRS3oyqWs101/7PwrivsQihOm23N2cew7ea3rTBku3Zx+4piOc+heHOeg3PsCjtQ8lP989vLtx07/xyEAkJ13l+F3w9ReIZDG17cfv55ikMeMezgjbjskEB4yDJlyNacY1HY16EtG+0pcfRBtzKKbYrDtlf4X3x82x55T6Nr64JXd/Plr3xvVOuO1mkVjPzpT3/igx/8IL/4xS8455xz+OEPf8h9993Hzp07qcpfE+VoTkQwsubFp/mXPj9dJZ5hyzVh4CONhQtTuDBREerRA4KQFWWKugc3gxezEyi00kCbMrzGhiJsgiJOKb2U0YcLg16lnF4qGVAOT83XpTqoj3egu3MIt4VwCyy9cOJ0PuQREFNC7FZmYinDu3H8IkGYKEFieMwcabOElFVCFi85TSfu9RdnmgD47BTVdBBSnMcEieMhiz3khJAVHvYzlf3KZGxFO6zNqrAop4ckAVJKyYjHTBEWtbRRTi8eMnjI4iaHC4MsHlL4yeAjhZ8+yunP14ORJEmSToxPbPgLX7312+O6zdMqGDnnnHM4++yz+elPnQJQtm3T0NDApz71Kb74xS8e8/EnIhjJWDZ/e/fbSCkl/O2cK9neMINYqR/Lc/iYDADFsvHlsoSMFJFslPJUlAl9A9S3dVHa34fqTTuBggaGppDTdHK2TrerlP2VjbRUNdBWXoepj7z9ApdpUt/Xy9SWvUw/sI+yRALVBltVEZqCpakIl+J80bAKX1UFtiIwwtA6sZK91RPZFZpOSvWP6ljU9keZ1dnJzK4u6gYGUM0surcTV+Qgvso+NI9JeiBELl5FLlVDzihDtVUsVFrDYQ6UltIWCdMf9tPrD2Jog89RETYVVh81Zhc1Vhf1RisNZiu1VisucggBtqVh2wq25XyjUhUFTVHQ8zdFgSRu2lyVtGlVdLgqiGoholqQuBYkpgbJqm4nYyGczIUuLNwih1dk8IosPpHBY2fxmTl8hok3Z+HN2ahJL2YiiLAUFGFjC7A1FUtVsVQFPCaW28JyqZi64txUFdN2Y9geTNuNpWjFbzWDYZ2Cmv9WXrgpdv5bshAg7Pw3a1B1A92TQXOnUXQDVAGqwFZBKALbcmPmfFhZP6bhQwjNeWw+4wDkv/0zuBywEdiK81Pkf6ruLLoni+bOoLqN/DdSpRiPCqEiLN15Tax8ZsBQsQwdy9BACIQQqM6jnC+wSv656llc3gy6O43mziEUQFGcjIqiIPI7OSyzUvjWLlSErSIMD7bhRuQ8CMuFKhiShRAowslkqIqNojiZDVUzUXQnu6HqBqg2QlHyGSiwFafFzqed8+XCFmo+o6I72QlbRxECTbFQVRNVMVGUwn5sJxuhiPyRLTyfIdkokc94DX0+zpN3sgbgpCOGfq6oFiomiupsv5AFUZTBHNSwbRQyHkIdzHgIdfDY5r+nFLMmioWi2oPZmmFZKVHMZBS2L2wVW2iDr8XQjFAheyhw2ln4WcgGqfmMilrIstgw5O+hCi0oZC6EUIb9TeE5F/9PDWYxnK9hg9tTnA+awzMCypDHjWDYKzEk0yKG3H94tuKwBwzZXv4SIoc95ni2cWwn8oQ9fWsTX/7aHeO6zdMmGMnlcvj9fv7yl79w3XXXFZffdNNNDAwM8NBDDx32mGw2SzY7mGWIxWI0NDSMazACYKRSvNwb5fyGWlpiLfxk8594oCNLVgk6JwQFFEXFparMDFVzXs0CJpaUE9Y1JvrczAn40I+jf9yybfoMk21dnTzfvJVtPR1kDYtqBao0Lx49SNT2k3P70BUVVShotkJEV7l8WgVzJzWgq0cPZgpsIeg3THqyObqyOfYkM7RkDGr9Xuo8bkp0jYCmUu91U+NxHXuDRyGEQJg2iqYiFOjMGTSnc4R0jck+D17txPbttw2kyZk2kyoCx/W4zlgGn1sj5B39888YFrs6YpgChv7PCftcTK4IoI1hvEQqZ9Lcl0JBQVUKCavC9gR2fp+aqlBf6sPrOjwrdbyEEKQNi1jaJJo2UBWoL/Xjc49925IkSadNMNLW1saECRN46aWXWLp0aXH55z//eZ599lnWrDn8cvZf//rX+cY3vnHY8vEORkaStbJkzAwu1YWu6uiqjioHykmSJEnScRttMHJanmVvu+02otFo8dbS0nLS9u3RPIQ9YfwuP27NLQMRSZIkSTrBRpfzH4OKigo0TaOzs3PY8s7OTmpqakZ8jMfjwePxjHifJEmSJElvLCf8a7/b7eass85ixYoVxWW2bbNixYph3TaSJEmSJL05nfDMCMCtt97KTTfdxOLFi1myZAk//OEPSSaTfCh/ZVNJkiRJkt68Tkow8k//9E90d3fz1a9+lY6ODs444wwef/xxqqurT8buJUmSJEk6jb0uysFHo1EikQgtLS0nfDaNJEmSJEnjo1CaY2BggHA4fMT1TkpmZKzi8TgADQ0Np7glkiRJkiQdr3g8ftRg5HWRGbFtm7a2NoLBIIry2otKHaoQscmMy4knj/XJI4/1ySWP98kjj/XJM17HWghBPB6nrq4O9SiXVnldZEZUVaW+vv7YK75GoVBIvrFPEnmsTx55rE8uebxPHnmsT57xONZHy4gUyIpekiRJkiSdUjIYkSRJkiTplHpTByMej4evfe1rstrrSSCP9ckjj/XJJY/3ySOP9clzso/162IAqyRJkiRJb1xv6syIJEmSJEmnngxGJEmSJEk6pWQwIkmSJEnSKSWDEUmSJEmSTqk3dTBy5513MmnSJLxeL+eccw4vv/zyqW7S697tt9/O2WefTTAYpKqqiuuuu46dO3cOWyeTybB8+XLKy8spKSnhhhtuoLOz8xS1+I3hu9/9LoqicMsttxSXyeM8vlpbW/nnf/5nysvL8fl8zJ8/n3Xr1hXvF0Lw1a9+ldraWnw+H8uWLWP37t2nsMWvT5Zl8ZWvfIXJkyfj8/mYOnUq3/rWtxg610Ie69fmueee45prrqGurg5FUXjwwQeH3T+a49rX18eNN95IKBQiEonwkY98hEQiMfbGiTepe++9V7jdbvGb3/xGbN26VfzLv/yLiEQiorOz81Q37XXtiiuuEHfddZfYsmWL2Lhxo3jb294mGhsbRSKRKK7zsY99TDQ0NIgVK1aIdevWiXPPPVecd955p7DVr28vv/yymDRpkliwYIH4zGc+U1wuj/P46evrExMnThQ333yzWLNmjdi3b5944oknxJ49e4rrfPe73xXhcFg8+OCDYtOmTeId73iHmDx5skin06ew5a8/3/nOd0R5ebl45JFHRFNTk7jvvvtESUmJ+NGPflRcRx7r1+bRRx8VX/7yl8X9998vAPHAAw8Mu380x/XKK68UCxcuFKtXrxbPP/+8mDZtmnjf+9435ra9aYORJUuWiOXLlxf/tixL1NXVidtvv/0UtuqNp6urSwDi2WefFUIIMTAwIFwul7jvvvuK62zfvl0AYtWqVaeqma9b8XhcTJ8+XTz55JPioosuKgYj8jiPry984QviggsuOOL9tm2Lmpoa8d///d/FZQMDA8Lj8Yg//vGPJ6OJbxhXX321+PCHPzxs2fXXXy9uvPFGIYQ81uPl0GBkNMd127ZtAhBr164trvPYY48JRVFEa2vrmNrzpuymyeVyrF+/nmXLlhWXqarKsmXLWLVq1Sls2RtPNBoFoKysDID169djGMawYz9r1iwaGxvlsX8Nli9fztVXXz3seII8zuPtb3/7G4sXL+bd7343VVVVLFq0iF/96lfF+5uamujo6Bh2vMPhMOecc4483sfpvPPOY8WKFezatQuATZs28cILL3DVVVcB8lifKKM5rqtWrSISibB48eLiOsuWLUNVVdasWTOm/b8uLpQ33np6erAsi+rq6mHLq6ur2bFjxylq1RuPbdvccsstnH/++cybNw+Ajo4O3G43kUhk2LrV1dV0dHScgla+ft1777288sorrF279rD75HEeX/v27ePnP/85t956K1/60pdYu3Ytn/70p3G73dx0003FYzrSZ4o83sfni1/8IrFYjFmzZqFpGpZl8Z3vfIcbb7wRQB7rE2Q0x7Wjo4Oqqqph9+u6TllZ2ZiP/ZsyGJFOjuXLl7NlyxZeeOGFU92UN5yWlhY+85nP8OSTT+L1ek91c97wbNtm8eLF/Od//icAixYtYsuWLfziF7/gpptuOsWte2P585//zB/+8Afuuece5s6dy8aNG7nllluoq6uTx/oN7E3ZTVNRUYGmaYfNLOjs7KSmpuYUteqN5ZOf/CSPPPIITz/9NPX19cXlNTU15HI5BgYGhq0vj/3xWb9+PV1dXZx55pnouo6u6zz77LP8+Mc/Rtd1qqur5XEeR7W1tcyZM2fYstmzZ9Pc3AxQPKbyM2XsPve5z/HFL36R9773vcyfP58PfOADfPazn+X2228H5LE+UUZzXGtqaujq6hp2v2ma9PX1jfnYvymDEbfbzVlnncWKFSuKy2zbZsWKFSxduvQUtuz1TwjBJz/5SR544AFWrlzJ5MmTh91/1lln4XK5hh37nTt30tzcLI/9cbj00kvZvHkzGzduLN4WL17MjTfeWPxdHufxc/755x82RX3Xrl1MnDgRgMmTJ1NTUzPseMdiMdasWSOP93FKpVKo6vBTk6Zp2LYNyGN9oozmuC5dupSBgQHWr19fXGflypXYts0555wztgaMafjr69i9994rPB6PuPvuu8W2bdvEv/7rv4pIJCI6OjpOddNe1z7+8Y+LcDgsnnnmGdHe3l68pVKp4jof+9jHRGNjo1i5cqVYt26dWLp0qVi6dOkpbPUbw9DZNELI4zyeXn75ZaHruvjOd74jdu/eLf7whz8Iv98vfv/73xfX+e53vysikYh46KGHxKuvviquvfZaOd30NbjpppvEhAkTilN777//flFRUSE+//nPF9eRx/q1icfjYsOGDWLDhg0CEHfccYfYsGGDOHDggBBidMf1yiuvFIsWLRJr1qwRL7zwgpg+fbqc2jtWP/nJT0RjY6Nwu91iyZIlYvXq1ae6Sa97wIi3u+66q7hOOp0Wn/jEJ0Rpaanw+/3ine98p2hvbz91jX6DODQYkcd5fD388MNi3rx5wuPxiFmzZon/+Z//GXa/bdviK1/5iqiurhYej0dceumlYufOnaeota9fsVhMfOYznxGNjY3C6/WKKVOmiC9/+csim80W15HH+rV5+umnR/x8vummm4QQozuuvb294n3ve58oKSkRoVBIfOhDHxLxeHzMbVOEGFLWTpIkSZIk6SR7U44ZkSRJkiTp9CGDEUmSJEmSTikZjEiSJEmSdErJYESSJEmSpFNKBiOSJEmSJJ1SMhiRJEmSJOmUksGIJEmSJEmnlAxGJEmSJEk6pWQwIkmSJEnSKSWDEUmSJEmSTin9VDdgNGzbpq2tjWAwiKIop7o5kiRJkiSNghCCeDxOXV3dYVdjPnTF4/Lss8+Kt7/97aK2tlYA4oEHHjjmY55++mmxaNEi4Xa7xdSpU4ddNG00WlpajngBNnmTN3mTN3mTN3k7vW8tLS1HPc8fd2YkmUyycOFCPvzhD3P99dcfc/2mpiauvvpqPvaxj/GHP/yBFStW8NGPfpTa2lquuOKKUe0zGAwC0NLSQigUOt4mS5IkSZJ0CsRiMRoaGorn8SMZ01V7FUXhgQce4LrrrjviOl/4whf4+9//zpYtW4rL3vve9zIwMMDjjz8+qv3EYjHC4TDRaFQGI5IkSZL0OjHa8/cJH8C6atUqli1bNmzZFVdcwapVq474mGw2SywWG3aTJEmSJOmN6YQHIx0dHVRXVw9bVl1dTSwWI51Oj/iY22+/nXA4XLw1NDSc6GZKkiRJknSKnJZTe2+77Tai0Wjx1tLScqqbJEmSJEnSCXLCp/bW1NTQ2dk5bFlnZyehUAifzzfiYzweDx6P50Q37ZQTtkCYNqpbKy6zE/0ovhCK5iwTQiDSaVS/v7hOMh7DFygpTpMSQpBLp/EMWQcjA5oL1MFtJ7K9lHjKB1dJGSAEroC7uCyaihL0BovbNtIpzEQMX2VNcZ3eA80Ea6pwe7xOm22bVDpJSWBwgNJAb5RguARN14rr9HV3o7kH95Xt6kP1uXDlBzYJIeg90EL5xIbiFO5ERydYOt5ASf6gCfr7BoiUBlFU5+2bTaeIC5uausHnFtu9jVzMjaI4+0+k+mhv6qBSHzxGuVgKf20Eb8BZls2k6Um1UT3nguI6Xd0dWP4Eofw6tm3R1dJMeUkFaM4x6oy1YWcyVPurALByJt371lPvrsSluZzjmLOwVRdlZYPv+a3RZvTaStxe55ikMim6YwMESgb7VaPRDEJNoGrO0C47k6Oisx2vNrhODLB8aVz5TVuGSdY0iJROREHJHyOdvmwAPPnXQwhciThVVrb4WptGFs00UYaMa48qFrpuomnOdmxDkMNFxDM4xb4vlyXrS5F/ObBtyAgNHbu4joGKT8ngyT8PhEXWUhG4yTcR09bQrAgu/IUFqHELjzDR8u+HtCLoDyigD36Psg0FrzBRC00SCv2uMCn34GeIbibwkEIprGSBlrSdcf7FdQyCpCluBoEHjbDqQs2/j5JoJDULVbXyz1Vg2YKsV4f8e11YFppuomIWt+1KmXizYsi2wbDdWEO+D2ZdLoSwcOnOe8ZEkPS5Sef/nwG4jRhuOwaF18O00YxS3ObgtrEFmmkP+3DPKSpdHg+F758WCimvH0sb/HwoMbK4cxnU/JY0IXApCrY6uCWdNB69v/h+tCyBW2jo1uA6tu0lnosAWvE4Wm7IkBtsj6rhsi1cllVcFlFdlIk+XOrgOll3ElSjuE4qG8gfj8H3nyZ0VH3weWRVNzGjDAXnOFrCxlByWOrgvtwijU6WoUpsnUojXvxbmDoJIpj5528DptsLijK4e2FgkR7ySoPQIGcFQRl8bV0ig9se7AlQlQSC5OAxQ8G0S1CHDOFUUdEtpfh6CGFBiYGpDu5NURQUVcdWhryRFQs1niz+30eozm1IVYwS20vCXQqFc4giSFYHKWvr40vf/j6nwgkPRpYuXcqjjz46bNmTTz7J0qVLT/SuT3u9/7eN7P4YtZ9bjOp3kV3zKK5H3kdKX0zgaysA6Pja14k++CCTH3wQz5TJdLYe5Be//CVht4tb/uOr5DJpHv3J99i7bg2zL7iYt7z/ZoKhAPz4TAjXw0efBODudf/BHVsf5Mtn3Mh75n2RrqcP8Oj9+8jZMKfWz7wL6niWtWx8fjOaV2P+rPnUtLby0+5f0lRl89U9l5CoP4Pmra9gJw6A4sJXU8vksxbzZG4VT6tr+VzFB1i2T2PH+n2sDV0LisHc6QmSExR2bNuKMeSD51Ca4ackPhV3rpSsZxfJ4D4sfeRuvNFyu1OUlrVSYvtJ73srRqKOfYAQWcz0aqzsBtigoboaUV2T0VyTQZnCtpc3Yhv7sYwmhNUOio9AzWTKZvWypTbMr2rfz2f4PmezBoCIH2zTTX/3DHp2T6en3SRdE2HvgT24B3qO2sbEngwNi9oZqJjKLw98iFzSzTxPP2cpcSpTIbR4LaqeJujeQaDzFbqTPbR73HRVuNnXOJN9jdNpr2rAn05w4ZonmbtrI0r+DNtSvgd3TSOexPkEYvVowEHdpteOc87eJ7jgwHOkvRX0lc+lt2wO0cgMbM19WBtzOCfP3XUunjzXz0BAZfHOXs7bsBZ3cjfC6j/sMUM/WJoapvPwsvegmznmb/s7evpF2ipTxAPmYY8Lp3Qu3zOTiv5p9JeZGJVeFCOHnoyiJWLoyRjuHoFd0ogVrML061j+HBmjhEBsKm4jzNl+jUpV4dn2NgYyu7CNJoTVXdyH6SshW9OAHQmgZtNoiSh6IoqWSpBj+Hj+LBD1VmKFJmAG/JieHIpQiXRZBNt3ktaiGC4Vr2kTMEvAPZ1MeAm25sM2W7GNJmxjP1k7TjZ/HM2SCNnqeiy3j26Pj5ZIOQcqJ9BdXkPpQA8XbVpPTaoT0+9Fw0XGbqaieT/hqEZW96AKQTDnxgpMorehhlzJ0NP8kXkP+Ttw6AqaB7zDBxge+grlKCXqmkRzWTUtZdW0lFaR1V3M6jjAkqbt+I0sIxFALBCiuaya5rJqOkNlgKA22ktjXxeNfR2QSnDAN4GW/DptkQoUITizeRcLWvagC/uoZ6yM7mLdpNlsrZuEUF5b0j+UTtDY20ljfyd1Az3otn3IGgYDvgDNZVV01oY44J+AqRz+f+a1UIRgVvt+luzfjs/Iv6K2ipbVSZSleO6Mhex1Tx/VtkrtHmZFt9LYfpBwrwvVHt7GBJANCromBDgQqWW3ezpZxctH0g+Ny3N5LY57Nk0ikWDPnj0ALFq0iDvuuIO3vvWtlJWV0djYyG233UZrayu/+93vAGdq77x581i+fDkf/vCHWblyJZ/+9Kf5+9//PuqpvW+E2TSx7i6e/NVPOfsd76Jx3gIAOt57HkZPD5V3rsQ7s57Yf3+YUPKvpHo9uL+5E9Pt4uGPv50Bt8LZcy7jjFu/xEtP/YN/vPASim1x62c+wwP/9U269+8r7kf3eFh88cX4dldgaQnm/fu7aNu7i7vu/Qq5uE3A6+IK13vYlagjLlzFxwVU6KtaQ8O8R8hmS+jvm0BHrBTtwOOcu90m5q+kLVyKUEYOKKIBA0sVhE1w58qx8aCopaiuyfjLoGfxPp4sX8y1nY9SslMjtDvLnI5tZM0Sdte8g56KJcO+SSi2SWXPKmo7nsZ0K3RUV9HfoNAadPH49Ldz9tanmdLdhCf/JUXz2PhrcgTqMugHNFwrPXh1P511VzIQmEvG20HW201JVw4j9TKIIwQ6ihvEyB/tilaNy38xLlGCbqfRhIWNihAKWXcQI/sKVvYV0vWTMIOl6LF+fK0HsFSNZ8+9jO6yKia27mNK824qeztxvp86//2Et4Gs70I8ShU+4XyF2Vdt8dw8nbTbg2JmEcL5oE/5Skj5S0ZsY01XK5e8+DgTOpvyS1Q0zyIy1TUI1cadmkhrWYg9dW7aSxUqY4JpbQaN3UlM3wHAJNjXgLC6scwD2FYr3ZEwK5dexP76icP25c2kOHfbWmZ3tqCZBnoijpaIoVomzvdh2LDgPFaeexliSLEjp42P0NDVj67Womu1aGoViqKRcZcwULKZ/Y1lHCivpTsYoTwZo6Gvk/r+btyWhQLYR6h9GEiavF2/kKDwsSe+iYfVLexrnEHzhKlkPT6EriPy2ULdtqgb6KGxr5PqWB+aaaIaORQUdNWFrrqxVIWMenjQBBCyPCzI1FJtBujNtrNV7eLlKh8tc+vp95djJA85cyoqQncVj0XC4yPnGvlE1tDXydK9WyhL5b+pWxbu3nbcfZ0IVSNRN4n9E2fRXFZDVzCCOEYxSFXYVMX6aeztpGGgG7d95I/9pNtDc1k1B8qqGDjkfWapKgP+kadoukyDMw/sYEHLHjQhMHUPraWVHCirprmsioR35Ex4gc9OkVb9I94XTCdZuncLk7sPcugztRSFbXWTWT9lNhndCblcInvYesdi4BoWxGiWRTidHLadnK4T9w5vo0tkUYcEsc5vh+9dCAWEgjhCy0zNeb+4TYOz9m9n3sG9WCWweU4Da/2Lna0KCxfGUZ+bgQtbGcwUacKk2u4sftYA5BQ33WrVsMeFxQDv2fo03/rUN46y9eM32vP3cQcjzzzzDG9961sPW37TTTdx9913c/PNN7N//36eeeaZYY/57Gc/y7Zt26ivr+crX/kKN99887g/mdNJZnc/wrDxzXG6Dp799c9Z94+/Eyot56M/uwshBFuumYNRIZi2+AbCn76d3k9dSHn5JlL9OhsrPsnGA7tJxwdnEk0582zc1RPwP/VHOpUgA74SktEB3JbF7NYeDjZU06tAQA/z9oaPYQuL+w78AMRIAYSG5qonEKrDsM9G5L9y+Ku3Ujp9JYGqHexstZjz8yA76ipJe5z7Fa2aELNJ+2vQkptwJTYS8x+7S03RKlEVPyWZJJqVPwErGomSemzVCYoCyXaCiRZioUmkfM5/FM3K4krvJaUlEIpg9aILef6cy5m151WueerPI+5Ls3V8dimZgPMNSRE2idoshs+Nr2U3eiKKL5sDrZf+EgvN8qNaflTbg4KCattUxtNUxpKUJdK0VNRyoNyDrQqSvgA7Zl7KzJYY4WQ+oBEWVm4biBRCUUhOPwOhaVR2dVLbtIHbP/gpWmrmDmtjMGUysTvKlOZdTNq9Ak8u47wm7rlEgyGePnsWOyZNOuLxdBs21X29eJK7UFJRAr4GtjXOIudyjuWs5jilvbsRdgyhquTKquktCdMWqSh+6A2l2ja10V6q4v1o8T70TAaARCDE1hkLEaqGZpmcuWUNVZkkL809h/6SMABliShntOymoa8Ln5lDNwKoZoBn59SwqXEKADM7DhBJJXilcQZGvhtiWuteSmOFrIqCUD10l5fTWlp5hDZaxW/SU3o6qexuQo0NoKTT5MqrMSKVpF0eWkur6S+bxI6yMEn36JK/bjNHfX834VSi+EGvqgYlvgEirYIz+hQmW2XU2+W0qX2s1/eRVC3aw2X0l05ib3kFHYFDcw/HVpKzmNbXR3X/Qcpj3WyrncTm+mlYqopi28zetwVfPogB8PnjtHjqOFhSg320apZH4bIFi/os5sQs1CGf/hlNYX2Zxs6QduQH582KWpzXY7KgI02/Bb+b76Wp1Hn+1fEU9TmdjaUuLHXwtOkyTRb0xri4V+UtPTq2As9XWzxXafNqpARDcaEJk5qubqbt28mU5k3YdYt5dPG5dPucNs2ImlTHUwTTSUqyA6i+fl5omMpBtzNJolHs55+5i7lsObzRx5DGy1YWsIlFbORM+pSKEdfThMEstrPQfIXZzbsJ6yYuowQ11IevrA1NGzl4LT4+GyLQOw89Uz5s+RZvFb+sWMIej7PfKqOfqO4jqzjHdXFqPQu2N+NOOMdUFyoBoxQMPwFVIaAq+FTodcfZXB+ltbKc3YEpdKk1jEQRFlONZmb29rH45d2c99Rj7LtmNu+8/d7jOm7HcsKCkVPhRAUjqVQThtGP3z8FlysybtsVtqDlWysRlknDly5F9erc/YF305tzTl7v/OLXmBCpZNW6S7HKYeo/ZjPx2w/S/b4ZZCaZrGyfSq/hJFE9kSwltSn6dkQQQkERgrr+OB3hEixNJSgUztxxgEDOwApW0VRWTWdVFVdO+CcA7tv/PYTiI2SYNLY20zRhPgk9BWKwb1T3hKkaKKWv/hqwnQ89YbVjJv+BZfU6KykBXP5zmTztJYKe3ezY/S0sAszc+UfKu1eR8LqxFY1tsz6A4Q6R42UiA5vIiiAZXUcoColAkGByDNO0lQAvnPsOVi2cS31Xgg/+Y8+QY96fT4k3c3hyGeIzzgBNx9u6h1m79zC9I3rY6O2cppJyuwhmspgabJ2osGGKQls5aJbKhK5SNs9+D+sXXoBuGpy96QWWbHgOt+n0aRsug0SkBr1iMgAHXRH+vuB8RIkLRViUZlpJuKoQQsNwDWalVNumob2FiS07yXi8rF9wHpamowjBnIN7mdzXiRCCpsAOugJxSrJe0mITov8cYr0Xg3C+XQu3ijk9hDXB7/RrH4E/m6axp5369v10VDfQXFFLzDdypqVgUWot7+z/E537FmPZXmwUXq2dwvrJswafixBUxftp7OukpyTM/oo6AM7au5OJnf24zRxoBhunTGNHTeNR2+hOZ6A3zcTUNkr9LlrLG2n1Df9Qrc908dbeVczt2s7eQCPPVV3IrvCEYVkCt2lQ399FY18nJZkUJSV91NbtwvbaPM/FbOQsdGGRU4+ealctQSRnc/WBTjQ7x9P1ZRwMBIaNuVCETX2qnaX+Z5jMXjpap9PbO3wmYK1dSr1VS7vHzR49A70KmZSJIkwWuPdxvjkRv6eMH8708Ey169BmDN9W0uDCHsHZfRZ+8+gf40ld4eVyjRcrdNr8xw5iZg/EWNgfJ5jO0hqspbnEhaEIwqk48xNu3tqjMCs+2IVhA4/W6fx0uoce7+D2qzJ9zDXWMi+3iTNCG/HqWRDgTtUisDH8naBABg+tNFDHQbriJdzXVou/5ww0O0hnrUaN7wxW14bJaiO/Z4Iiynusv3JF1x7inSG0ZBovgh7KsHSIuLzUmdPZkoviM8LYuo+MLcgKcOWiqAyglWVw1zURLG8BT5xW6hmgdNh+NCwmib3URBMk/Rqm23mu2ZQfZdtFBNtn0l+9Da9Q0FQTw9eLIUxUj42nzESJNCP0zBGPu43Cc7yVP/N+ooqz75nmAW7dnmVeWwWtah8tag8HtA5yR0uPCEF5NEVd5wCJCWE6yjX67Ciq3ojiqkcVKmVtpej9B1DZRbXPoDGzl8zVn+Ki668+xrvj+MhgZBReXnsd8fhmFi74XyoqDs/2vFZWxuDFJy/CdqU4b/FzuCtC3Pmea8jkBxlNmDOfyxcuZEPoywBUP1XG1PffzY6vv4uV2lQAXKbF/PMXYM3/C4oG2V0T6HsqTKc2+HKVx1Ocub8TV75f09B9vLz4S/j9YS4MOdH0k7HdJK2JuOw0lV0baa89D2FbTLQ28Me6h5jbFMJrOB+oJfVZ3CULSOwW5KzdOAlHDc17FkbJHPTaX1Ey0YVHCGL7z0dpO5esluCg65v4W+aRKLuC6Vo1CdcA9y76DqbmdHdU9ivono/x6qzzuP7JR6hvOwAu8Cg5DCXLFK2JCjuHqWo8EgyRxGRGzuaMjEHcqMFSSol4AzR7A9w7awlbp0yjNJ7iu//z2GHHvi3ooa00Q8Q6iNC7sF19CFTSkcsB8Cq72ZtcxgejKSqyd2EYEWws+kU7kxMGfi2LvyqHtzLLq34ff/HWk9EMKpUoCvC3CV9mX9nZxf0F0gNcsO1hzjm4llL3LTzl7qJB76AzWMpj884l4/YQypjEvIPf0C/evY2SWB/N1TU0l1WPmPae0N/FBdtfoKZrLbnINIRSjo3NTvduFNsg0lFPOuOjZ9JsNtZMQsnZLG1PoPY30RdU6WuoJmJFmeytZ0/ARrdSRBL9zGvZgI4f0z04YsC2DHrUNNvrGtD8DYBCU0UtSY+PxsReLuxfyZm9G0hY02hLTkVXc+x0V7PirAso68lRmrHZXeMF7/Bv1KptUbavn56p5aAovOO5Hj6TduHB4vlwkqdqXZiqwkFsTNsgpMQJG/3UuPdQYsboSlbSGy/D6vYwoa2L6ZPrEY1n8mKlziulGrkjnJgmxeJM721jTp9gYjRJ3N2F7e8mVBKnxGUhVBvVEngMH1rf1fxmcg0T0gYBU6MlkGFnUKVfeJjufgXNk2UTi0goI3/uKLZNeTJKeTJGJJngytKHmFG6HoCe7nm077oGt2njthVUxUcy0M/GpMX+smoGAkH6e3UqtS4+OvUuJoYO0tI1nZk7zyNiX8SeoMZzlXqxSyrjbyddsYUqOrhm9duZEvOTFUlyAlIIcoqKpSh0BD1E3SoJN6AoeElRog3g868l6H6WLcpUVnIRSVeICVYnmgI5l8AdSVLa2YO2zmBJahsTtUbC7ilUeFO0KaU8HJzLznAAkerjsrIVTNC7aNt2PtXmRERJiopINb2mwXNlJl5XkgX2BlzWTkSuC8uVAcuDJcrxWDk0MiQ1jZ2uGkK+GDOrdmMLUBWwLBdbt3+INWIbWTXNPN8svLZKuesgGydWk2awm0TNlVAR93Fprw93ySKae1ooG/g/WvU6msLzcbk0lFSUd/ROos4KkZz6JH9VkrB+Am4rx1vmdjOxweaxA1Hc8SksSi+hxHbRYz5CqmwHwjcYONiBJN5gPyl7ArmqSViGTSoaQ02VsyjwXiKBibT39rClp5cJIs5MTwZVgX7XRrLubhqmvh9twdm8+PAPiVo7sfQU4bJJePAx0LUPM9WH3+3F5fGSU/w8VbKImoSXD7guQvO6OdC0l3h7D91aL4ZqEhdpROQABm4GbD+ekA/LF6LN9tFbPZMXe0ooT3RzU/s/KE8n8dYvpDTio7Ojk0xzgKoZAS7/lw+QaG2j6+UNqJ0G05a/C3fgsNFEYyKDkVFY/8r7GRhYw7y5P6K6+u0jrtPV9Tj7D/yMeXN/hN8/eVTbzQ3E6L53Gi5T4LvmJXx1k/jRje8c1o24bF4dPec7g1SDq71Mqv13Hnj8HhJ4mBnsoWFVnPSNU0ieuw0AJeOl5laL9soyXp0yibCngrMvm0vJn/5BZouzztbZH6KzejG1/hhL3E4KsKnhWzTvu4GB5ODApxm77mVC+wv88kqFF+boLNs+laq2LIjhH+6hrB/FfzZ9NSEenHkPM5t7eOuOeeyfM4O+ToXJ2mx0Uc32qhf4UPZJVvV/BUXVeSTUzf7Jv0foXt69YT+XbEjw/m/fg1AVJnWl6F3XxX2+b3KGupdP5j6FaWj8xP9rXEqcB0oCfLWynEbD4JGD7fQZnyGuVhG2tvDRSc+yvvZjZEouQhMmS/du5CMv7WPqnh1k7Bh3zjqPJyrP4YfuX3Kd+iy2CJBSXLQoGl+u+wZ7qibw3c0/4EpjNRn7bF71xqlJ3kyNdjde7VUAevUI+zJnsiW5gJW5Sna7fOjCZG52Fx8M5vjsW6+k2TeBC3teZqdvFp0B5/0YMgQuW5DGQCiCrO7GVlW0WILQq918oLKWqQmbLy704TcsPrLpXmLZErREjH2NM4l7vTSXVWNoOgsP7iGqePB0TeCmnIu9QZU96dVE9DRpofP33BwSwgPY+KYH6Z8SAeD9+3PMXP0nyhIp5lZeylO5V3h7+Ar+o3EfMzoPsL5xBtVaL+FntrOt9iym6b00WxG2WDXkakswFpRx6eb1TO9rIRWo5neLnUHm2sEk4W3tfEDbRla3CSQj/Piy8zFcLv57Qxqjfz0/XzCNlooazuvM4EGj3avw2V1x1pb6+d9pHhb1mvxqnZMZbBYmv1VynI/OJbjp8XXQVv9NUr+vY1Z7CxqHfyRlPB46r/535tkTaVf6CSsRXinTebFSZ11YI542+FgPnNOd41nlJUKdzVwevh5d8dA6939ITHgJ21J58cX3Awr9tpdSNUOnFaTVmMv38eJD45ZFPl6ocgLHr4kvMYOd2Cj8IvEeakssNthn0Revgt4M7y77E/5Ihvv5J9L4sHDxFfEfVCnO7MFW6vm88iPnCVg2X5lUy/JptRiWzbf/50nWbNzPXNcUzp7xKJXz/gaArodonfokX9vawr83xbmiawBSFuSge8bzxKY7Awxna3dSOftC9ErfuFw8dNfub9PSchfh8GIWn/UnsAx46utwcB1c+O8w/bJRb0sIQcftL2PFcpR/aC6+mWXF+4zOJJ0/eAU0hbqvnIvq1dm+/Tba2v/MhAk3kkjsIBpdT031tcyde0fxcZ2dj7Bl62cAqKhYRmXFZZSXX4jHU3XY/kcSf+4g0Ueb8EyPUPmR+Yfdn1zXQf9fdgNQ9t6Z+M8Y3XZPB39df5Av3v8qhiU4szHCJy6exvJ7XiFr2vzzuY1869p5p/QCs6dNBdbTma47qWnLSh1xnfaOB4jHt9LT+8yot5uOtTOhI0tVT46+zvX0t7eC4vTxlecD7R1DaqdkK3NsfPEZEngo0bNcXrcTt2UR1wenRAtvBlHlxl66lOjkGfTV1kG2C6vbWaez8kw6qxcDNqUNLxYfp1RnKZ99N1Vd6wCY7HqSKm0ARQg+9pjNDS8ZPDFvPxOvbiPYkAAg5wkwq72XC3Zspqf0f7m24svEvX1smqIyf9erXPXoY9z84gO0uZ0xG7O6ltKceg+KqhMrsdncWEXqwMfI7L2Ze8Jf4eEJby/GOW0xE6EpzFEOALBFTOJJ9Sw6VQ8PV1zEFckUftum2eXiCdeHSVuXoRsLed53Ebs8OijOIDhL0fl0/99YWL0E//mf5fZF7+SJynNQ9BgL3E513z7jkwxkfs8/lA+wecIUukJlbAwuRlVy+LUXOTO5mUb3v+HVXsUSLn5b9n7OPveP/Hz2WfS1rWdh70re3f4A745lqDQnovluoNk3AYA1JQvRXoxTvyuOzxTEXAq9HpWUx0Pa7cVWVSb2tLN00ze4OL2aD+43OL/H4v37c/T6dPpELQB26QQem7eEmL+My7Zt5JpNL9JSWsUG10RW5wyWiwRb+p+gfu+LWDkbn2KyzLULl5ICVKKRwfE6L1TqLKi+msVMJeStZl7oMnaEdIJp53Xt9weJB0uoSzWj9fdiGXHO6/gHS3pfpsTl/B/YGKgHwJ/o5F92OOM5rPoAdbNwAhHhYW1jJYbLxZJekzPbO2hWe5jW3QrA2qDOjRsT/H51mjP6dB6e4HQ13HDQYACbn5BmgmXyFfyg7ySHSUW6hqy9lC+89Wre+7ZvkKpzjrE5ZBr4xiVLKMXJIO3W2skkm7mgx+Lz2zJ87B9R/nd9lutaDejZSkLJ4Jk5m9/lP93KWpaBAFWz0TSDqFXOHo+PxsZNVGsDuBD40EiKDHvT/0aw938oib9CSEQBUBEs96/gnfyFubvXYK5OYu4yuPTb27johXXczr/xwc5fEHlxH1VKJyKfyqjjII09uyBjgabyvdYuWjM5XJrK1256Cz986U7O7N5DqGFt8XmaZozHO/YSdylkr5yJV2wk/cCX2N+6joOetsEPmekJXFX+cTvJxGKbAchlu5wFmguu+A585InjCkTAmW7qneUEIJkdfcPuS291un290yKoXh0hLLp7ngKgqvIKZkz/CqDQ0fkQA1EnwzQwsI6t2z4HQEP9zSxc8Evq6t416kAEwDvT6fLI7oti54aPoROWIPZUMwChZY2vq0AE4Iaz6vndh88h5NV5pXmAj/5uHVnT5pJZVXz9mrmvmyvdv6mDEU1z0n2mlTziOoVAxTKPvM6hkn2DYxnSPXvobXFOvGFvDbOCiwBoiymYmXztgrDKtqgzBfTCqibcqo17Uj1WzfBviLnKLFnNOfkYmOg9nZi9/WTdYXbOeC8AVRMexeePFh+jKl58uwaYt+0uLlz97ywuu5fOs9/BU0ucTMk7Vwne/nKWfUGbSVe0kQhMpEudxpSuARJeUKdkmZEzmGCYGDq8PNNNSTKJbps8N2sP+8o2oaDRZswFBK/OcmPOLSV3XhVWmQdLaNw/7WLIOl1JlqpwgbUbt2JiCx+pjIalaNytXsm/zPkGV535G+oSzqyNJ4cMY1gVcjIXXnNwUJmu2NiU86KdYlXJJAAmVv0fU0xngmbLdOcxc3MLiiPge7X5tFrvImq7UPQGEH5S1gXsSf2MH0z8ACnNT9JXAigoIkdCC6G557LQO49+72AfftbtpldAyY5Obnmon088HuWqZ9bw7nUr+X9r1nLj6qe4cusaFjWVca45mFH7p6YkihB0+52galf1ZFAUGmIV/PnMZfxu8WVsnTCFUL4mwRldu5i/9xUU28bT68EvPETUDLPUKAKww4PdP80BFXQ3WqlzLKags6ZcozTljNPpD4TozU/dvLj3eZpmTEZP51gc28g04yAAUctDqxUCBWZ3b+R/Xk4xeyDLolbnPf3c5Jk8N28umi24dUeWVs96UGBSbzsuM4fh1/lEMMeqM+7gkTP+SqdPJZCz+X3XADcbPVgoaLqXfhLs07rYoDuzfs5qez/vmDiRebld+NtawaVzy7d+yL/c9p9kPB4OVlVRbjtviD4licvjnFwURWFGKE29riJsi11ZJ0v4l14/94sctmLii03BG50GwDmvvsS6vhCXTF3JxEmvUlnZzPss53V9SoFsJsDU9iY++EKOcnPwRCrUHkxbY1X72VyddLNMy1CVihH8S4ALlrxAq/IVpkWcWW3p3qkgfCjAe6w/4Hm2gwahkrYF39rrBBSq349/yRJqxVN4wu3OTIu8tugOAK5K9NF/758A6Jx2Ge5Qe3GdVGpwBt1Y2bZJPL4VgGyug/FIlnvz2ZDMzv5h2ysEI765zv/jgYH1GEYfuh4mEllCKDSfutp3A7Br1zdJJvew6dX/hxA5KisvZ/r0L72m9uhVfrRSD1iC7J6BYfdltvdiDWRRAzrBi+pf0/ZPtaVTy7n/E+fTkK9jNH9CmJ+8bxG69vo5xb9+WnoCFIKRo2VGisHIUdY5VLJvf/F3c+Agva1OFiTsqqDKOwnL48MWKr3bIwC0barCUhVCPpNZIaceQuSaK1HLnBOSK1UJgDi7hly+DkQOC/1AO8K02T31OkxXgGDsANXBv6MNmYyvWD70PudlDviihJU4XVonfzw/zF3LnOU3PmPT9VwZ/f21bPTO4IK9rwCwYqHCGVaOg/YMLk05z//hi2p5df58tjUoJPywtf5+tHyVg0bfOtY0OicMJaBhLC7nmjP+gd+dA5fzYau4VT7s2escJ7OO6QnnJPdn42JQFAaUEA0J58TR6j5QfB4tuvMYjzUYjHT5SmjD4jv54kUT2MZb1O0AREtc9E1cy+qyZ9jvF8XBkkkdPjGhjQ/ULaQz9zM2GD+kz/giAa2WdL7uRdxTgpaf4bMhPA9LV5jg1jgQGPINQ1VAU7g8kUE3oSuVJWj0UJ6McU7cR13aRAF0oTBbGxzEWGa7uLI1i2Y5x2xP2IfLFhDPEfdrNPR0AJAqc45jba6FjFvHlzV4995dnJkPbCZrvQS9cecbrDBR+p1j8GKFjhJw3i86sCpk4DUNbGDAX0KnO0IkmUFRBG/9x0Ng2fiyBi01TpCipG22Wc5A0b1KDxNdL/H5dXsJ5DIk3V7WNziB4g0tBt5EnCa9P/+6mMzZ5aS5lToXkcrN/KNsBgBvazdJWib/vOVvvAvn/bsn0A0KbNVaSHsGUNMaN6Ve4cOX/RahCrj2nWwNldIyaQpblyzBowfw4sbCptaOEFRD2KaTZmzAabvR/CJdQSfAj9pu5kyCWO1LAET2O2UEHmpcTEuwghKXky3yelIsyr/WT2GR3f9hFmz/OF4UXK7hU7y39s5gpl3JdFPjIuEEePtrp+EpqeWSJfXMKd/pvC+7ZhPPNQIwLbIPBfjalDoU4MGuAVYPOPsuuegifFOcIGBoz9QEDrI45Mfz4x+CbSMuexc9vSbu4GCmNJVuYrykUnux88GvbecwzegxHnFsnmkR0BSsvgxmt7NtcyCD0ZoABbxznGClu+cfAFRUvBU1/zpMnfpvaFoJ8fgW1q67HtMcIBQ6g7lz7igWMTxeiqIMCZCGZ2viLzhZvcCSWhTXa9v+6WBaVQkPLb+A/7phAb//yDkEPCe8jNi4epMHI85AnaMHI8nh61gm/O8yuO9DR3xMZmAwnSqS3fS25FOA7gp0XymK35kS2bO1lESbj/49YUAwszFanGDgu/QsNL/znzjYca6z68k6acV5gwlFoLYnECh0lzt1S6btvZ+e/ZWoDJmpYXpw5we4ukssPBhElR6Saj+Pna3ytzOdKXEffKKD7nX1RIWXOd37sRR4/CyVOcnF7Ddu5cKE88EsXAZnTvoQ5rxLATjbPsjM4ANM9b7IGaF7SblUdMuiKt4HikJNVR9XznilWK1UrfQyRXFSwV1GhOlJJxiJ5XyQNnFnLcKZUiZkaghag3UJvEKnJleOSxkcvNbhDfJDpYmYqlGR7eaM5ErOTTsnqC6P87z+OM1gq3/w9Y2qJgfdKgv7nJN6hVVDv2ljAwnVeT8MiDCWncNUXGwLzsLvVqjRFZoDwz+oZgiTsFaHEIKXvVnCLieYabAr8OZPul5PhDoRwlTg+RJnBtNN27vx5ZzgIe32cEF/Ox/9y3+yYvmNfOnunwGQKAsigDKcE1AkZ6J5w0yyqlAFlKlpfDXONrRcC1q3s+3nSy2UIbUSkqrz/k2pbixVo1eNEDEtNMtGsy0QggmxLrpKnQCmKtdLqx3CFCqW5WJj+XNs8jgn2QvTE7m4KcflrVk+vifL/2AR804o7qtqvxNIiVofcSXIOt0Z6Htdq8F1uRxXZrPUoxJHsFNznpetCPZNcALw8uaL8aoVmCUaa69/DwDneTX2T6ijLJ8V6XD1cIY1CYA9nS9j2E62QCgmMeXvGFVOiv3sGfV8rvpJBiY6J7tg95no6TJa65z7g7pz7Mp08Cgq/WqCLd49WPj4S1AjXlIoVqdgC+d45kQV761yTmhVA04Q9nygkc5YhsfTCWZXOMHYc/3T2dTjBGJBd4pFdSmunljOP+crBP/H7lYsIQhceAHZs5yZWMqQObYTaOHtmk3y2edAUeheeB0ufy+qNqQSaWr8gpFCF01BNtt5hDVHT/VoeKY4n3OFk38hK+KeGEIrcSOEoLvbeX2qKgdrTrndFUyZ/GnA+fz1eRtZuOCXaNrR65Qcy2DX0WC2JteaILc/BqpCydLaMW3/dFAWcPOesxsI+48+E+t09CYPRgqZkaN00+S7Z0zL+TZDtBkOroWtDzh1r0eQSw1WeyTdR+9BJxgJuypQvBEadS+6z8RIutj3uPMNqrokyuTAYBo2ajsfNq50Bf5+p0sl7eokOaT/z+5LEw1NRuhebDtHOLaPaHM52pBiZqrlxZ1zTpLuoPNTVWNY6gAA//e263h5hoLLgnMe28qHtjozVF6eqVDqM2iKvZdMuoYtqUsoNy1mZKfg00qoCZ4JwBlJnVbXIq6MfI9a5QC6bVKdzOLNVxBMEkC4KgePjVulTHG+iewRQYJWknKrF1DQOtIEshn2GNO4euACIubgLJOQFeDygfOw9CHpbE8lDepWgmaCazofpSJpsTRfH6NDd06SOW89u3yDH/QxzUL4/h/XbXI+eDRFIR3vJO4CK/+tqz8/nW9vySxyqocZbgW3qtDiHd73Ot92Tg4ZMYDHFUVTBJgKEeHHm59uG/E5ad/V5Rp/Dw1gC5updhh/IRhxebg6vopIvBl3zUKmVr0FT84AXcXvzhIxnSBjy9QG1s9cghcXdfkplQ2m89Nl7McVc9L2TcHhNV8mRZ33rdByuEQOW9FI32gyp60bhGBGRx9/vMjC1p0TZaXZAyhE83FXU9MZZGwFr9CZZ9TyX3tM/nNLjm4zxzPYJDOD2YP0AGDa5Dw+7uZfsBQXNYkUM+M2l+LBO+USAP5GjpQxWHSu09tEomITitCp2nEjmxecxZP5sv1ntOzBBOqTToNUFDy46FFiPDA5Rp//HmzVoH/iP+h5b5x0frtfv2oOiYHHyAYP0u9rQxUapc2XUR922uvXnOMfdjnvjafDL+OZ+GummTlsBdxe58Rp40VVnON8bmOC8nwaPNsxAMDm8in8YX0Lr3a9QomeJGV4eT7dwOr2xuLzu3rmQRRF4QuTawnpKlsSae5p7yUd6cUqh0PLp9ZzkAsefRAA38WXsnt7EnfQCfR03TnBp1IHnPLg4yAWPzQY6XrN2xJCsHfv93j11Y/RMuX7tC78MTv6P8/27bcR2+lkLQtdNPHEVjKZVlTVS1nZW4Ztp77+AwSD83G7qzjjjN/gdo9c8+N4eKaEQVexolnMTucLSuJF57PIt6ACLfTGvwTJ6exNHYzohWDEHPzm/HJTH60Dgx+U5qHdNNkEL5kRNlsBMEbOqFipwfLYWiZGf4eTKQm5y1E0F5PSPVTMcdaxDRXVbVEzp48SMbi99s6nAXDH6/HEnQ82I5QmNaQapNWbpLfcKaalJA+iChtXL5jm4GALLedFz2clC8GIT0RBc9LMcyN9/O0GjV11zrUpFnc534IfPVtlWqKCV9M17MoI1ufexbkpi4DtfBi78OGzbVbG3sU0u54cLnQsJmVamR2zqO2zCaZsEpSQUCPDjo9H2Q/AXm8ttu5iuuZ88GvtacraNMJWNZfElhC2BkdeB60SlkXPxRgSjLR6qrhA2cY1HX+nxEox2cpSZtkYwk2XPQmAtF5CYkgVyYyuE4n1ogUGTxZmYgf9rsHtxj1hBLA2NA+ACfkgpdUzPBjBU4kQAg0f9aqT2o5RhYKCLx8Q+gNOl8fjtS761TStqd2YWGjCOcFNdO1hkmsf3/3Cz3Gf83H8Uy7h4lbnRFnujZJTXMS1AI95lpF2OXU7JnY7AcakWBcIgZ47gM5ulJRJuTm8jTX5onk+X5QKnCC5s+ECSs+IcsXmJvTKfl6dVulUv7VsyrucgLjfa6PrWQofERNFCQKVZL7df9TjeBn8pi4E7CmZgNrpBIMvK+cBUNa9HwtBvSeMXjUbocD9ZFFFIZC3CUcep2vmPQjFpKR3ATRczUsDCby5LLkdTjfGhKTzvGoNJ7Bd5dpFIpygd2Inuy/5GN3T/4wSTDGhfjtut5v+//sRuYkGCPjrfOe4lR64ghlJZ8q7N1/rwZvvinkm/DKhXIhrE0EujfVR4v7/2XvvcEuvs7z7t97+7n76OXPmTNVIo14sWbZly8aWbXAJOCE4NCcmQEJMyBfSqHGCg/2RQkiAYJoJBAgQ08yHgxvYuEq2ZPU60oxm5vS269vftb4/1tp7nyONiyRjLqxZ1zXXzJzz7rfv9dzrfu7nfvQiJC/GAV+md1Np6c/F0qf0fB6dWOJ/33WeK5TWJ63sXIpUNk92x6m5E039u2nP4V8d0SD4XU+scmr1AwA4Zu2yEWmDuMOcw/4/WivSv/XvkQ4K6vN6o4mJF2NZPkplxPF5vhKj173X/Mt4ZjwHZqTff4gzT/4Cm1sfYld9nP7cXXRrt7Oy+ns8PvtvKdwe4ZUa+A5ZkampW5/GeliWx003/gG3vOQvv+wqxi81LM8mOK7BXPzwDmUvI7pH39f6LYtf7KMXx1dhPK/ByFPTNKc2+nzLL36a7/vNO0fbPFUzsr55hk+cuor3nbkalfa44EjHxl6yl6KkxMEhtPVK35ObTF2xy9D+cP6GLeyljICc4Rw9SPQqwu8fxEob2FkdLFA1o0aPY9QgYmvKBMwNrfOY6++w2RuLsPxBgLWlH/MQjCR2HyEUSrq80vk0l7cyfurv2qw2dQA9P6d4ZBEqO7fimdh2XNZY6dxCtdSTebUMuSJyWStvYZKADTQFeiJ6kpeeKbnt8xW++ZM9dsspOtYYVPgywxYxpXLYsmcYHLuKI/4ABFi9nOufCHhNadEsa0zLsf14lQNM5y2SfczILK+0HmQq1/fkqBHuLmdXkaV6wktyl7ZxCgUtPJ1s76Bq48lnY2qSbbEHgDou641L2PVaBMBwvbRtDI4wFG/sW8TWCq7lj8BIITWrMkzTCCFIheRjsw613jaPdj5HLEwQtyRvs38Gv6jwo6dsbFP7/YJNHQCduuAvpm/ltw7+PXadCWaNsGBhbQu7KGhkMbO9Nk52Bjs4j7WVMJfsZ+vqiX5Hp5trzKBXvHcPpui+qaTz3Rk//0aL0tYB3o1Sjm4aYWRQMD+v0w6eN+Aqr4mH4Od2HuSfMOD9wqGyB4z0lUfbr+OtjL8TrkqZ7D7BXYwDeuWqaRZmt0f/XzhwmlpthyLYZfOQbiHx8vgAr3oy4QUbZymLnIWFBSrNMXh8oLXCutWh46xTTiqwSrqndCrz0KH7mGpI1s7/gb5+6yQfXJji/sXTCCzeuPYCTiJwTN8j6XXpiIgnvGUOdi7DUoK//cT7cF0NCH1Hg5WwchylSuyGrkrL/BbBddchbYfNzYirynv08165AhRERYWdpKWPkXxuxGL8g8VpTlR8dvKSn1sP6VGjNE3mPmW9BIlFQJ/Ul/iXXcbjG3rBNH20DUCteoJKeAT4yohYpczo9bVgttXSabU0e/ZgJEk1mA2CJU5e9h9YePK7mXvw7+MWs+SVdVZf+HMI83UcgpGZ6ddccF9CWCMdyVdq7K3y6X9mFUqFd7iBt3Rhi/uL46s3nudgZH+a5rF1PZE+vNZDSoWUGcr0KRluc+bcKWwlCDKHrilnfOrwovGEnA10AGnYzVGJlZySuJWS8KaS+666gemrdpDTinjT5tE/mGfj3joSzZyce+JmPtAtkavXARAYMDK9tUXqNRnUDqJQHFnT5YHz0Q7t/tip0htUoTsEI3pCjI14T8kJjrtPcEVQ0qsIfvLNgp1jAT/3DY6u8LBC5kzwP1iUfKL/BppGx1GRAae7r+AWfAJcznp6xfeC3TNYRrB2YKdgJ12gY49NdHxp0jfyABIbbJuwGICxoH7IK7nSUOcNxqvLijjCKZUx2CPbWPVnqVq7NF29Wj1UbQNwPr2WttSTTtFXtKtjMJQ5HgdWN3C9MVtSThxkK9v/LD+3pHP+t+AgECil6JvzuvHJh3nVQ58jdVI+fHXIbvNxqlZGoSyk1McKDDOSiJzP1TNiR1Drt9lKz7NSalAQCsVk7DLz+JuolLBm0kA39PTf7clJHq1dSm55TGXbHDQmcpshTG/oFd0lG+dw8nPY4XmszYS5ZG+PDIWV62e9OHl6BEY2cou7H5vj/FF4UtiUjtZRVLKcZt7BUQUNv8fBpQeYX3iUy05+At/NSVBc/sTHeYQYkQvEnk6hG0pP5ifOPUHdlMS+kM/Q8tp8WI6bp+VXnuOGCR2EhJ1z9Ojn9bl+dp6B/XG2j2q/jR95MOW2Vb0AeNGR6/Esvf+7rHuITmqg5yiBU9W03+Yj30g6OIJtF8xP/Dnx9Rp4L176nbTLkvdd/hiDqftxlcN/tN2RRqPwejxurdHMm7zs6C1cW95OFuS47tjsKvZfwLzxIcqtTwCQ+k1aL3kRx5eaeFbGpUIHdFav5Bsyj6sO1HlsVzMdUkajahXXErzjhAbCf6ZeyT8Wv872If3O3BOcZENprdN9r/wOVl72PZw35dVBU6dpKpVjVCp6v18J3Ui//whKZbqapfkCfW3PIU0zLA2uVU+wuPitHJj6Flrnv47F2/8ZVh4SVR/hwQf/JYPBEwwGjyGEw/T0K5/zdXy5Yyhizc52GXxGM9a1Ww581Y5/cXzh8TwHIzpIDlMxw/RMVki2B9k+YetQO7K1tc4dl+9w/9EOW2tnAFhbW+N3fud32NgwQSYdf25gynebztj4p5jTE+HaJYd49NpvxBrox9COQmRh0Vv1KY3T6uZ2k0zB1iM6p1qr6slpZnNzlKLpyYgg76MAR0m8dKyitjp6ErdcOdJExI6+FldUEQKCaIJqXmVjSvL+17V5YkEwkVXJlMdyVetdMlUyI1IqqZ4sKzLgbPdlvBiXP5/z+HeXfx+R5XPT1iPkmeYSLATsTtF1xj07bNNWPi0OY6UxThxROfMwNeMY+4hbUlE+kpKKHN+zWn+Gf2SlqD29Lta9SXJhs1TpYAvJQcOMnElPkuw8SI8aXj8l3dOMLHNc5na32euIXXMn2GKPzgfotjSz8nIjBhZCkJlUTi2NObFxnoPRJ3GDnI0pDT5XZZ1h4/bAfC4h46NT+lmGScTjR07ysUmjWyjqHLzrB3HyOk9WBP/oBR4SxZFCMJVKdidnsJySw8lZvnnlD0eC3nMLUOb6Pl6ycQ47LxBOm+baDnOx/vmWPSAmQ6ocpaBZ22HGiGFzZ4r3uxl3m4ZxhdDAa9avkLseE9kuTa+H62acOHE7rdYG0kl4Asll7SeRi/o8Isb3dd0ITK+IH+dv8YdMqG3ewB8zEXR4ordCZ7BJeSLkkc4Pc+XkowAsHb4P246JogZb90xjpTZbl/wBnw9XcBS8ducQh8pppj5dIBDkFNy79h6mJvWzmRYullUilUUeT7D8hF5h1xaepFhSCBxmZl5LtyjpWDVWrv05doMNGvYYaJRej8ftNVpZi+984zdyyaGSdqs1YkYATnk3jgSWcX47wklI/RaVF76QxiVNLpl4HNcqiLIp8v4cL6lWWe2knOmO2ZydnbH3z8sn6ny9EQXXVI9JNKA6x2FkYbRMk23uORWCgoMnWySZBh7V6iVUKkf0/f8KVNQM9SKN+tX4vl7EpOnas97fEMh4xgNkyET4g0UW7/4BBA4bm/+Xe+79bgAmWi/CdZsX3tlfwXAmA5zZECTIQYHd9Ecalovjr3c8z8HIfmZkpT2epJbb8T4wMgQs59prPHi0x12Xtdlc0yW7n//853n44Yf5zGc+A0CQj/ezm+ljNPYIsIppHYweq1zCy8+3iXc1YNhp6W0z30I5Apl7JKbx99buEkXcoFrTYGR6a4utSQ1G/IEGDMMw3ZR7erNEOkh49YKoowNk5OhrmTENnXZ2DjM/0JPgn9Q0QGvF82yLlL+c1JNoR+UctDs4qV7VWVj4QZXPLAX82jHFva2TfGziJg7GZ/fd42rbor+nLb00Z1mqSwlXTzO9vo1UiiszF0fBtq14Nym/5G7QHIxX3q2BInXHr6sjFUpYrHnTLFU7HAi7uJakrSo84Ge4G8sss0SY7m9pnjkulSLF2wNqFlOLlWC/ivBqt8G343EjOmAroDAVQZU0wcbFQnLj2UeYMkzYedkiNamUoYA1EimfntYAxc8SNi9b4q4F/UwqMsQfLJL7O3zEeZTrb38fZwyL8ILtnDQIOXJ1j5dmt1MVFq45l/OHFMutGRLHpZLnHOguUYscTm6dZa6rn+lHFhq0TSVNblnYdsms0hUiA3eCDS/lg139PhTWEQBO4HLvlS9nKtuh6e8v75R2ymmZUwtTokumsQQMlM+u0RBtGDByMj/D6/gTfo7v5RBP0vI7HOic41+kq2xe/7MUZZt6eIww7LB0QHuCPP74TbQbE3jNeRDw/pnP8UCQ4eHwmvxabRoGPFh5nMvPxdRMmfm0eYY9OQXKpr3bZG3t+OicJxovJrcaFAq6qoF0Eu687LeIvD29QaySgdfmiHWEVtDCO3LYgJHxNo8kAdXqpYThIZTKqM0/QO7WcC+7gjM1i5MndQn6qbVLAUFiw/YgYyM5NtrHzo5mVKRMefChf8l3Jj/C/1LfzJ+fOAOAvQkf+cX/zhUPaCBwYP4UfkW/N1fcWqMo2oCgUjk6ZkYGX16apiwTVlbfS1E8Pa3cM5U0jcbV+KYCLXsOzEia6c/6ngYj/tEmwnxvG9b1XH75TwEQx/qezcxcOEXzVzmCPY6w1RcvIL5AW4GL46s7nt9gxNkPRlY7Y93A8m68zwxtuM2uMZCSFmxs6fxoYio4lpc11e+rcQDcTvUxmv44dVJM6n0tW0sc2mnTi/QXtzfjIkNFbHCL2rx85I+hhEP/o6+gWt3FyTMa7T67EycBOLJ6x77rqu9pD67MatqrFwyUDmZ9s+q7JNDBaXtriXp81BxHH28qmSayEzYr+hpl6XLQ6lDZIyoNJwL+0+U+fSP22/AmmRGblCLj0RmdW5/uFETOmKnJrCHTcCWO2yR0avSqDa5YyTlueuT8Fhm/mVeZyMb6h7oQ3CQ04+KXapSOWPFnWaq0OdLQq8vH8jnOTbnkkcN5lkapMc9U92SOS+A65poyJIpWDrtT+10XDzmTfB8BDbSZ1sAGx9zWapbw8uwydptL9Pyx8G5ZNhlO98M0zUAk7BqA11u6hsta91Mz/hih8ijtmOUb/itXrdzNkccf5rPmfG/e0PfU9m1k1aZi9EZtJLmvuP/YpTwxo+nlA8lBji0HXNo+x/xA37O7J102bL2PwNY/WzRAqe1oXUtmjLZKSzMNVw0U2cSNTBURTd+85+aapZ2wm+xy1yVXc2yyytdfpd/nB8s5EmXTVvo9v0ztD5ATfptm2uPA9efotD+BZfksLv4TDiw+jGUplntX0d49QHuiRbCo/WU8d8D7ak+wK8z3zzBSp4JzHNy02Xn4jN63eSBtOYNEkZURZ05fj0r19vMH30THCFCTvn63gsYaj4f7gafrJkwbMz1raYluo7GPGTmb+2xkxShw1g/od/uJ3ZTlNOeqqgZVO229OLh/R5/3iQM3MJxid9t3kiSrfP7ut7K29kcIYXPFZe8ApXU37rIgvece+Lxmr6qX5HzHO17Mm3/sppFeJAgWse3wGadpHjv1Th566N/wyKP/7mm/GzIj9cbVI0fTIaB4NmMIZIb7Eq6Ff8LoqK6aZmHhmzh+7F+YrQUzM7c962M92zFka4RrUXvhhTvaXhxf/fH8BiNPEbCe2x4zIcvtaH+axvy7l4xXTFttrd9Izep7Y2ODNE0JTK1eqQTtTDMbTdNxVFoZRU3n8Vc5wES3Q54c0dtPl6z9x5z+N+hJVm5r+lxIPaFub78cd0cx11uh2zxG6QREQnLpylhwC1Bjj4jRBEWvXrA5rwPnrqP3dzjs0T11gDhuIpIj+Hs+diCrM3AGdAxgscqAFgn1PW6KL+ptIoVgYFxhN9xJPCvmaLjOoKbBwXRXEO9pdR7bIQU2uTrCpD9P4DRIw0Wme5JbUps3KIfX4XK116a1p5lP3XdoVDXbEJaKSZOOOO/PUXczrmzqSXC5U2NjooYqLc6Xh8hNimay2wYgsx08VwOqNG6zKTST4Jvy41qqg8M9Qc6fkrGqSvInP8GaD7lZiVfTmACX1wyW+IPrX8bHL7mGT3CEvvLZNffeN+malILU0/dnahquSj9Pw6TxVPMcZ6/+H6T1c2Tzgrvnj/F5oZ/NC3b1fRYC7GpJYFoXbKFI8gYPHz7OqRktVJ7PZji2YnOifZ653KQAQ4vzxuJ92jzYwuyzY9WZMt1mj26A8vU9WlhOOLEtaVqHRmCkXRqdk52Sd5e548Q1XFYN+K5bNHh9vJzicWlanofQaBlRq/mrFXQ4cvnjfPtl/weAS47/ayxrhmpFP49Pnr9OvxeVCs6CDrJVJ2Yi2uD/ep9H3dgkaenzP6c2+ZEX/VP+yZpm8Rqe/t6d26jzgTCnFAl5HuL9rxYTn5hhfu4NdIoSJztDek4DiKY/YCD3V8F5XgJ9kFLSbTRQloVvjxcmA2rc0RmMwcjhOzn0yv+XR07/HFeqezjMGQBuPaC1D32jpXnVlUepVodMTc7td7yedvt2bLvGtdf8KouL30q/r4XqQaKDt7uq73eUncGvOEwfrBNF2vBvuK8hGEmzdYqizxcbSbLCyopu27C+/qf7KmXKMmYw0CmzRv2aMTOSbT7rsuEhkPH2WLW33nCM+tct0fg6rQE7fPj7uOzSn+DKK/7L6JhfzeEfa9J8wzGmvuNyrL+Bfhxfq+N5DUacpziwruzuZ0bKPV90KROkLIiz8apqt68n7Mz8TCnF8vIygaX//2Q2jcTCQbJpV/hnDLjd2wIBeebRo0Gz2yFMNMMhZnLue+CV9KYMSNrVX97prXvwsy6516D4/RdxYGeZbZOiidw2NvsnjnCPgZJlgqJXK3nsWpdSWawYZqDaq/HY4zfr/RQhLzSGYVY6xbS0GLgDEqdPbqUIBE4Z0NwDdP7pfQ/z4q2Cvq0B17Krg+MNXocJIzRtRhWktZ8GPeMfBTxmgykqdh3P0ZOrM13wQ6LCD6uAW6fXcPaAkUau6Fb16+qWBe5AX/Njrr5HVUtHv7V2SLdqtBVyyVi7Q8P4beSOi2cCe4ceaazZrNlUB+SZrl5t3hXAu0jYVSXF6j1smo60tizxixwbl2v7IS9f2eaBhaM8lmows4ZEqpJQmRocIXDKAqssaR64G3eQMzFo6+c79RDbDb2KzqYT7p5pcg8FEsXBzGMylcR2QKUWEdr6nLdRrIYH6NXrrLamcZMUV9kcSCa4dPc804btWA8EO+hrnjfE1Ha7Tqj08b6udpDbVl/GDY9dBr7x8FhJmNsscO05WgaM7GR6spZOQrB9igePnuDyasgLDk8w6yZILO7ONUNzpFZi1Y0+yLySE36HY7dofUTWegMHD76FNE0JK3r/j3TmCAf6+5ek+jwm3B6OzJGhYOGbrsBu63fukXySx1sHKZRFpmz8QF/fdjzJQ36ONN+7xv05M8vXIYRNuyiptH+fTlcDe1cMKMr96QrbG1DmJe12m20Dtn17zIoOqHJHp0+jfh3Box5CKCrTj1PN38OP8BMA1GpX4KQa5B5cqPGiY5O84rIZGvVxU7ai6OD7C9z4gt9jakprwIaVLI05LR511gVgURQdssyk1QwYqVRMR2+3getqNutLsSNnnnw3yrAvSuWcX/6t0e/6/YdQqsTzpvH9ebNPC6VKsmz7C+zxi4+hZmSYpgGt02i+9giW6VwthODgwW9nfv4bn9UxnusQQlB/6eK+dM3F8dc/ntdg5Nc+2+e9O/+Jlf5xkixnJxmXKi63k6c5s0oZk+7xHejH+vfbsWLXuZZUVFi9/z58oSfFM6n+QrZI+UWRcSclv28mu51Mt1UXccKR+HK9kvQVSVJHmRxm1tVflkq0zjHTF2XVeyWH715m25T0Tjt7mmeZYYs9pmfGsdVqlbRqKZt2lQ2zKl47fSuRCdb1MuEb+3qSjrvXUclg4OiS264xgKoUTfa0iyH06rzjnpjC0oF8zdXsT8V+ksPlFIk9QF7AkvjBQIOPaXeSqtOgXujPNZv6frZLRd+Um5YmtVArYWCqTXbsLVSkGY3H3HGJ7mZaY1D4lJZAIdiQc3RqLQD8rgaaSgjKQKc8+rJLnGrh6pxhsJYyncseNmmbK0tUsosyq91KmiCAv5zTgOctZ21EN2dYWDKBBQhcbGzj3BnmKbU4IvQ2KRKHaqKv03UkfaXPr+EElPFBesDjvg6yN+yWbPpTtKqdERjZRHJ6Sgel+a0Njp45A4CozjIlBA6CAsWWB6nU+5ks9WfPRbOjippudDnNZJadoy/UJ15IdgYSP5EoL8E3lTtZrJ9D146ZjDY4O3eAkzV9r64wLqq5AbxLQY4KDQo1mMSzc5Y/cZQfuPc/8fve/4MQFkmyq9kIYG0wixXpY2109fev5er38JJLLoFugZvb5CLnoURf92vP3EGlCAkMGOk6GVXznXMQeFmGu6jfiwe2HsAbPESvpz8rhGJQ2Z9Kcqr63VhbW2N9SwMAZ0+aJqLKHZ0B2SOPMvkz0P6N72Dtc9/BcnTjSNM1N/t6oq4+hze/7Ai/870vJnBt6o1rRvup16/iphv/gFrtMgCkzBkYB+KZm/8OOA61F91KGGqAPRg8Zv7W21QrYz1M9ctI1exlRQ4efAsAy8v/m7LU19Y1/iKN+jUIIbAsB9/Tz/vZeI1oEKPv3zNpYndxXBzwPAcjf9RL+cOpY2ymV3F+t73vd8M0jVKCONbBK8u6FHv0GAPjonl+d55LzzcZ2JfTu+9zI0v3tVSrxBtuzifQwfO81Ld8XSxhSUmUlywUFbK2ZkPq9W38UK/KkkQHkTDrcKRWR5DTry+xPv0iosocEsV14ulgxLLGToKWSaHIKbAFPOC0kEJgS4FjzNE6QYXDcpvXRjH/67Qg23oloXQYuPo8ur6eYOplnYBxba3wGggxZko2XKOctx7ikmSOreoyWfB0cdh91SMATDKDa01Sz6o4Al7aM6mIQhHbeuW3bmumACD39b0TZUFggOM5b5zzPRfpFXpj0GV1dhFrUNKp6dWq1yuwjGNubgBYlPf41Ky+tsVUA7jDpmpBBTY1oFLkdOZPEDmmv0+mg+hvH6tSCLg0qXP9sv7ZHPCfqWAJi67IRhU1QZ5RSWJCBpSJjVvod8G1x26voWOj8hYAd7VMqma7oO3U8WspgaOvYwvF+UnNQE1vbHL4SQ2eNhcOEE/MjbaZ/ovHERSgYDrRP39sMMusASPnMDqlqn63RSYRRYKXdui1DCgtfIJIp0Q6ToJshCjL4vJqSJIkLKlNwj32oQfcGOmN34ek0IDOO2XRtVp8cleDhzTVIucoC0nKkMhY/C9v6/vYMO/diRMnyFb0Z5701imMruMVaw9hlQFBYHRc/l00TX8iWVoIGIGRP334V0nW34hUHoNcA8jl+U+xdzSahk1aX2dtbQ1QWMFYBD6gxv39mJ2/+CgA1WCB9hMv58OP/gD/iP/Jievfz+HD3zMCI5XGWLDdMGDEcVq84Ib37gvSUfQESmXYdo3mlS/nkg9/iIM/+9+pVrXj8hCMDP1EqtVLRp8dGoF9Ma+RM0/+IkrltFo3c+KSHyXwD5DnO6yv6/LpoQ38XsDkPQfdiGZTJGCNmJuL4+L4csfzGozUDEOQ5jMs72raeJjhGApYz5y+js999pvY2V6ku7VK4Y7BSKokeZIgc8M+SB95RueAUzy6JsB5NTlKbqwVAVIJtuyjtKKEVdtjU3TZMezA9PRZLEtCZhOVGkh4+QDXgrKpA8+jJ3RXyxW75MSeDlsKhbJA2GMwIhwf2y+JQz3hP2BpwNAsPYRJg2zWJjhighTpAcDGJdDMCGNmJCxqeGIP0+FViexxMGr7epXoijMcTefZqp4nDZ/+it1XPYRUEl/VyJXmEq5vOUzFFVKnx+OppLQ0YNgVisQAucIIGS2ZUzP2/BvuNKVJTSwnehKf6Gxx7+U3Uh30kJaNUxbM9yWeYbVSIzrtJiWPTWsKfCk2fiqJseT3bOaEYGD1CS55FZvGfbWSJiilOFd1+ZMD+l78k+WMAPgpKhzAoqdyPmu3RxU1QZbhZymtXoW056HMfbcswaalwUgsc3RrO8kd8xo83Lijr3uzOY9vwMg25Sj1JOIeE7u7TG5tIW2bz19xRG+PpGIqqhoqpB4vgIL1pMmM1FqegQkW3VCD4EYWce29/50X3f7vqFkakHXSBs1YA7xIDNieWSC0BIdDj263iy0Ul3t6fz45FRmNUiUAaWlSX94OyrXZLUoyKclzXYXWjvWxtyd0CezujnFFdRIUmhlpn9Ur9MeFdoM9IvsspgV2EeD7+v1sq5gjrn5Hl1WVgRPgLi7ywPYDPPBwhaJzIwChr999N9ivs5g2Zn9ra2usr6/jOBnj9j4WU36dUsGWASPN4/qe1GPFYljl0MRlCGEz6Bgw0hyDkVr1JEI4FEWb7CkBvmf0IrXaSYQQuPPzWEEwBiPRKcoyIkl0KnGoFdH/HoKRCzMjSbI6YkWOHf0BLMvh4JJmR86d+zWUUnvKeq8afW6o4Xg2zMhIL+JNY1l/s5q0XRx//eN5DUaapkNjWkyzvKtXg/OlviXdpKAbx0RRC4BB1KS7tUpuj1d+uSPpbm1QKvPFUy71nqb9IwISo+pvVMei10LZbMcTpNkik/0e20LyUfcBBgMdlCantMVzsT1DYgK/IxNScnqBaS5mwEbbS1HooKIcC1mHcnY8EeoPB3j1kt3iIOXWpTxmzKNm98hMtsIGRyw94W1g0iOqNmZGjIjVkQF7h3IDBns8GzomyNtik6oM2arvEPvjV6xqwMCj1aN0sk1zPxxuqNgcUILMSTl96W+QKrCFvo5tIDHVScoYsNmyoFZo8NitNvjExhG205vYyq/V97C9xaPHrhpV0DTjAQtZsAeM6OvYtnbZNrb4BxJFkJeUQYhlAvlEqLCwmPTmRoZk1SxGUoAQvOe4T4nkOuXzs1S5BIc2kt8R52krZ1RRE+YpQZbSWj9I2nUpTFrDsi02jVnYjmFtLLfD3a0UieJoBBOp5Hx4mMDR23XsbFRhte10EcDV990PwLnpFhEpGyhaxlG2qarYZYidtlicnGTWmKBFnmbt1n0NbA71Nmhe8lLqL/xHvGrtY/pYaYOpQm+n6LE6e5gT1QBbCHo9/X25eTohsFJucJY5u9khF2Mwkhkw0go6CPMebOcFZanZvKG4+/FQsy/SMAuOk7ElqwRhhc45/e49mmsw9rcmC5JwBt/JsSyJUoJ2KXCrGlS2Cfjtk6/GXVzkR9//PrKtVwNww4sXmajrQFs1K45hsVazoueB06dPE8cx3p6yXtdtcVOrRqPfw3lA3+fJ600TvEjywpYGVEoq4gswI7btj9Iyvaf0gBmKV+u1y/f9vDYEI/3HRmDDdSfxvLHG4UtV1Dz55C+iVEardTMTE9qd9sDCt2BZIf3BI2xtfWjEqjQaY13LqKLmWYCRcSXNzJfY8uK4OJ4+ntdgpGF0GEnZYnlXB96pUhCYSWqlnY2smsvCo7e9Qe7sBSOKlfV1/KRHHn2cIOlRz/Uk3VcBZaaD3xF/iyUslkod5NajWaxonoluh3gqp2NFI32IZen9Rzs6P+zkfQrbpi9SMlESzjw0Or7w2khjw140Q2RLIGf2N3sSloNThzw7hrzrX7Ps6Il3cY/2ZcutsWTpALFq6/11RUhka23DME2j9jTgA1COx5ozDj4Dp0JseVgiw2KT3Xo+Ep0CzBl9cNtvsptrBuKob7PoWeQC7r7udyhnHkABoTT6ECVIpQ4OQy2NI0uqsT7/qFLlM+3DLCf/iIqpiJlsb5H6AcKwRq2oR6MI8IuhA60OFhveKkoJ+sae/WQnIihtfOMTU6mWVJ0WAPe19H2rpgm5gkocsRpafMB0Ur0cmwLFDxHzoJXTb1b3pGlS/DTB9xRJvzIqnxaWA6bp3Zq0zTPdJCnvZ9VUc9ywW/KofRLfVH5t7il56rrneLI+x9z6OlNbWyghuNd5kg3kCIw4SgNEf7DATUcXmTCVX11PB/e1wIDgeEBt6WU481dzVGiQ0E2bTBf68w4d1iYnuNyIg7tdDeK8MGPywG9wmbNJUWScmhwHotKUknv1jFmTztnKCgqpAfdurt+Nh/sOrueNGEbbzjlXttgepIgNfX8fKzWz981XTROHM4SBfjezvKa9ayx9Pn3l88fHXsp/e3KLux/QIu/yENx41SyuSSPOGIC1bSqFbCNWHQrRJ+vj1KLjNHhhs8pND96LkBL/0ktpHtPgqR5Lbm5qMJdEOdKkcMP6/gVB3YhYu9179v2839Pi1Vrt5L6fD9Mx/cFjDAZD8eqxfduMwEh8etSBdjiSZJXlFd3bZtj9FsB1myws/B0AHnn03wOKwD+wrwmd7z17r5GxePWrXyFzcfzNH88KjPz8z/88R44cIQgCbr75Zu64444vuv3P/MzPcNlllxGGIUtLS/zzf/7PR94cf52jYcq6ElVnpaMn6YYUNEwgXOsUSKknyKJ06W1vkzvjL37uSO7f3KGxfSdl+lkmz/4ujrGP38z0Sta1Cg66u/w9FEtGELsRzRBGsyzurOI3dYC8ZmX/hNQzzfEq8SaxV2MgjDfFAW2fvWNJJp020qRdyEvkTJ18Wk+E+R6vk6TlE8lZTiHZNud/OE/JRU5hWWyLKgu2ZkbWTPBdd4qRi9owTSPl/klPWTZr1v6fLbt6ZbXgfw9JpWC3YtIbhaRpyk5TN6AtNfgJTaXNO64KYPJOLDcmrUS0TMp+t5REpREWm/Ox4gr21hxOoffXrzZwLZ+mCTbNzjYoSWZARzMeEJQhnnEt7bva3n250qdSVNgS+rlc2o8JSxs71Qc/ZoNvB0RWyWN1Y3iWJSTSpzHQDMOvVhS5TpDxl6rP/ZRsCYf+ZH2cpskzwjQmd86TJTrQ23ZGNSk5fM40ryuMa627i52v8rAJti/YKblXXM/bbpnh861x/6AgHeDYZ/js3EkEcNX9etX+kL1MlO3QsvT7kpvOOl60xEtPzFOL9Pu24xodVKDBxaG+xDK5CbuqBZQya+KZXkS206ewYy6v6v8PmZHETpCBNuryKIn2dA0eiqfLluJ4rM36trIChF51b+cg7AGlBK85SVHqa3OcnPOywcZWRG2g93cKSejZzJ44ShxOj8SrSVzhO697O1WjfyrsbUrL5t2fGAA2telV8pMHaDg2rquB10Spgd2WAUNFsUOjMfbPmZ4ZM4CuASM3P6C/d5Vbb8U3zEc9kdxY1yApMimaoOZiO/un1VZTd7g+v/zb9PuPjH4+StPU9zMjumpGUBRtdtu3A+wpEdYjDJcQwqEso6c5pl6IFRmOpYN/Hxi7rO7Vi8CeNM2z6E8zLuu9yIxcHM98PGMw8ru/+7v84A/+IG9/+9u56667uPbaa3nta187skJ/6vjt3/5tfuiHfoi3v/3tPPTQQ/zqr/4qv/u7v8uP/MiPPOeTf66jWdMTXSQC1tqmLFAJGkaDsNpjDzPi0tnaInP2p2ke6fVxTfdeu+jwWWeOXFrsZHqSn/AShIDXiR4Llumj0T3ETOwybSboy/IFjlpXYO+ppuub/jJhtEk/qI7AyBYu762m/Gm9T9MaoKwJQODHEWVDMmgG5nxjpOkDszoZ8EAxyylKeqZL6fF8wOfn/oI/u+IkzbRLxRoglcWGSWFsuuMy556/g0KNgNZwKBSblr3vZytmVSREwZTw6ISGncggNGRMabts2uP35f0Niw/MCer0SJIqWa3HZKY/t4uka3xWhqW+buYQ2Ds0Iv0serUmruXRcnW33eWFIyAsNqZ1br8Z9bCUi2/AyMARJGWfvmhQyRpsGHOtS+IcT9ooY6h2tSn3/aitSOxhmiYhVpURGEn8mN937uVfWbujAuu28okda0+aJiOM+yTlDso2OiIvQZUuMtf3eyPTAVK4O9jFKg9X9PN+1c4WFRXxSNPme26usHFMB9QDG+ew/Q0+O6cD2eT6FnV8SiGxxJkRM9I3AMMfLHLzsSk8UyAW2wGp45KH+nyW2ntMZoIj+lqTCpYBI5aXYhc7o0qaITPSER0K00naFZLAGb83vnHeLFuK4wP9cm9lGa6j3/vN3MbyNUMWeXXKYthcUBFZNu0z2gV2g5IuioOtEO/QElE4gxfq4ydxlVumX0rddPMdzH0cjMbIDs9w+UtPgBC0XAfPgFXbVMpcPv8KALJsh/n5sRB6YnZsZGeXASdDj5sf0JUnWy98EU/YusbLlnDQCNIvJF4djrm5N9Bq3UxZ9rn7nu8iSddI003yfBuwqFUv3be9bQejiprNzQ/pZ1G5ZN82luWOttkrYo3jcxdkRYajWj3G1NTLR//fW3oMX6E0zUVm5OJ4FuMZg5Gf/umf5nu+53t461vfyhVXXMG73/1uKpUK73nPey64/ac+9SluueUWvu3bvo0jR47wmte8hm/91m/9kmzKV2M0TN173xGstnUoqe9lRnrWGIyUHt2dXYq9zIitOJ1L7HLM8uyIkPevXMZWoldMoaf3W2WHOV9/wTf6C8zEJY6pB11K5xBOgFgbp0FS0/GzEq8TBTU6mNbl/QOcdiWiskNplSAchFfD9iRl0GXHUMQiT1G5nnSfmKjyoXKSU0gyowM5XEQsynW6VYsTxk6+V85SBDqYbHt65WtJC2mVRF4Hh/1gBBRtsT8ttOXp835ELuKWLXpG+NnMFZXSGHkpxXpQ8nia82Bc8rMnPRppm+4TNU793iIdd42WASNtFG1THTQkYbzM5SrnE9RjHXS6tSau8KnaFgrBuQNa3DfUhlT7CQIxStP0HYuo6KFkg8pgnnWTEjkcK4SyySMLWyqu7OnPf9AtRzLhahoTCW8ERq52V1mzu9wuHY6YMs++CogcsS9N08p3ybIIZdxoXTdBli650U1sJSZN52lm5IGGvrapQZNf2Plv/O1zGZZSZDUdKJdWTyPsmIfmp/k/J17Bb974Bq4pDaNRHRCIAqWgcE06ZbBA1ROoUtBUBgzUWiiTtrtsawwibKFZucV+BavQ5yf8HKW2R2maITOyXq6Ti3FJ/KzZN4BvND5lEw71NBjpRCvYVomUgu1CYAWrZj8hUtpIE9xDJ6FzXrNnp5CA4th0FREExJVZvIreX5LWUO1dPANYA9klWPhDnOZdfONL18g9U2Hl2LieBnKWrb8XVy68Qp9f2WdubmJ03rU99esiEWT33Udj0KMfVrj98DE+149GZeaxYUS+GBixLJ9rrv4FKpXjpOka99zz3bQ72oStUjmCbYdP+0zVAJTcdKSuVI89bZtK+HQR62On3olSGZMTtzyNFRmOpYNvHf17r14EwBsJWJ9FmuYiM3JxPIfxjMBIlmXceeed3Hbb2MLXsixuu+02Pv3pT1/wMy95yUu48847R+DjiSee4P3vfz+ve93rvuBx0jSl2+3u+/NXMepmIu7bsN43nWL3gJH1nos0q7UyC+jv9vYzI67kvOVhmbp9278WC8mp3jQr2zqACuOzYYttZqp6cl2LJ5lO85EQMTYCWHdNfyZLA0SuV8qVeJNBWKcvTOlipPO7vpuQG5vLcMFi9sYuolGyXTEi0ixFGNvxRybq3KcmuJ8MHH0vF4uCa9KMoIy4zPST2S0WsQO9Gu26+u9mrvPrfX8He0/LeAAU9Ixx3HDsmKZXZ9U85weX0Der42auqJi0SiNXDJwXcH8MDzg7bLYcmmmbtccayNTinIj2MCOKrtg/wfuZx5x7hkakz6dXa2FbDlULak6D6+1L+OEHYkrbmLsN9HaeKakduIJB0cVNqzQHinNGJLQYOezaVWSiuGmnpFbYRCjumLBGz6qSpeTCpRrr4F1aFoWymEBwwKQlcuXSRY7SNGGeMR2uk8VixIy4bkJZOihlU2Q1NmMtRLbcHZxijU0/IKnp5zK/eYQfeTDlHZ/qMb2yQZAmXP3YXfp8Khu858o3cNcVV3FpfpBZ2Rg1E+wpnwlX6xK8/iyDwSlsJxp5jSxP6HfJThLmy3FqoprMgbRYSCawTEWX5SlKscOsp69x+J1cKVaQQjKsFwutMaipmMoz2VIc2NXpqNg0d0uSOlXhYRtm5MnIBQRlYdgkJ0FstAF4DAkI5poBUSdDWi5udcvsp0Z7WVfnIC3+3mfewEv8ZY5c8jH+n5u+Z2QH39qTphGmAizwFxDm3ZrZk5rxg/F7bvUk/b/8SwA+e8U1fLafckdnQM9UiQ3a+nsZXaCSZu9w3SbXXfuruO4U/f5DPPTQvwGerhcZPYPqfibkqcwIjCtqBoYZ2dn5JJubH0QImxOX/vgF9wswOflSJiduoVI5TrN5/b7fBQaM5PkOck+35S9njDQjfw2uqhfH3/zxjMDI1tYWZVkyN7f/ZZubmzP1+U8f3/Zt38ZP/MRP8NKXvhTXdTl+/DiveMUrvmia5l3vehfNZnP0Z2lp6Qtu+1xGw4CRroDYiNlqUtAwq7OVno00FQEybZJ2kv0CVluxHtQQJh1iucd4cUMDjpGmzNsDRiZ0cNnIq4RJQq3XY2ptg9iYlFVP6zx+tDWBbXrKhNEG/bBK36RpRGJa1Nspfm2XEsnkJds0FhJmKwnbhg2gSFAGjKz5VQbCRrgdhFB4CqZKyYm0oFJGXJ7qyXynXMQ3wWTgmRVZrsFGz9/ENVblhRxqOASJs3/y7XoaRB0Xy8ymEXVTSqqZEb1NM1Nklp5In1xaBSFoZrvsbpkUQLUx6kvTRjEw92d4S/3corU5RX2gJ8ukoQWFnmXxxqV/zHevNTjZ1Z+vpAm+6XnjFfq8Y9siKrtU8pJa0uG0EdnORCGrqoVISr5+VW/7ICVFS1+jXZZ4ZcGbjy3QTI0pmO2QYzODoIqgbs6xU0pCNWZG5hqbFOc9pLGGd92EzADdftyinbb0LXV3ESoldTLSSZ1Tqe3oVXGlX/C6P/4A/98//y5OnNNGWK1Qf++mUNjYXJsfHj8L6TOb6vSCk7Zob95Do7o2BiMtvYJ1kw51f8wMWMrFi+aZkQdGaRrHgcTeRRp2a8iMDKwBoRuOUjVqD1EmDJMm6zC5rUGHta21LXHU4PqF60fMyBNti8RxKaS+JxU3Zrqjn9v9Ju0yW/fpbOr30wv1NaRJlVXjtWKXAR4z/Cf37/HBb/4ghxqHRmCk6dp4ppxZmAaRtlPD80wH4Gkf27aZmppCsKf0dzej/zFdXfSZq67n9s5gHxjpD8FIV/9daexnCveOMFziumt/BcsKR72unlpJMxzD8l4AywoIgqe3uR+KWOPoNFIWPPrYOwBYXPyOUUXOhYYQguuu+3Ve/KIPjpqFDofjtEYALU23vuA+LjTGaZqLzMjF8czHX3k1zUc/+lHe+c538j/+x//grrvu4g/+4A/40z/9U97xjnd8wc/88A//MJ1OZ/Tn3LlzfyXnVjc+I91ST1gVwEXQNMzIxiAc6QCK0qYcFPsErJkrGfhNMGJRIXyONbrcOjvO4fpDG3GxTW1qFc9KkQi2i5RbP/aX3PbRv0AkmtoOVw8z/Z8dVj9zNdZwUo436VdqRIYZEQYoeXZGvbZNTokTmBSTXTAwXVQpUlShP+OpADtYxnJNx9/SRgALmaRWDDhR6uqGbTVDJTNsjatBxIaZ9DvBJo65G1ExZKosMme/qdm2WX0eFuuczM9w5UCL9JqZIiw0QKiWitiVKKfPfU3NpNSTs/h901yu1mTY4mQXRWacTM1jISxL3O2IqrERdyfG4j6pJHe3bJ40AKMZ9/GMCNktDaXuWERFl0bRQyQx52seiQW2skjyWfxE8op1HbA+R4EyQufKsFQ4CGkm+t+57VAoi1nzVZoxkKlXSALGzMjBDwzgXg9pyp9dLxn5cKz0daDxRYEwnXYLe5f6ogb9QU8beG2hmI7a2EpRMSLb6aoGI67x7PDkBJtS/7vZ6eJFXQpfP/fBuSeYbD4xMj7brOtn5aXbuKEOyj2jNfF7S/jFNCoxOh0Bkb/DoJNSFAWDgT7P2Im5dOJSbZGCMn/rUZYDilK//xVju99a0YZjUdzgRZe+HMvbAFESZ/CHl9+CqulrnrQyDhgdzdlQg4PZekBnMwIkTq0NaGZkZUUvAOwyIHeruIuLIzFuewhG9jIjBlQ79hiMeF7K937v9/Kd3/mdZPnO+BrObJA++BAIwZ1XXstqmrOZFQwq+5mRkcfIBdI0e0ejcQ1XXfXfGE69tfoVF9xuv8HZMYR4+lQ9BCOD6DTLy7/FYPAYrjvBsaP/7Iueg74HF+5UK4TY0zDvwgvMCw2lSrJ86L56kRm5OJ75eEZgZHp6Gtu2WV/fL25aX1/fJwDbO378x3+c7/zO7+S7v/u7ufrqq3nTm97EO9/5Tt71rnchpbzgZ3zfp9Fo7PvzVzGGaZqBCZJ1E+2GaZp+1hwZauUGBOxlRmLfxpUCNaxcER6uW3Dj5DLnp4/yl5MvQbh6pWSLTYrJhNmK/sKuxVvU+3qSrQ30z+yZkwQFPOJqEyI36+KUCd2wRmLACCYw+3ZGvb5NLgpsV0fuIE4pcn08VaYw7A4rA2bDcwgDRmZMuWYzh1ox4Cg6UHTsFrVU5+dTw4zkpnR1J9zCNRqXyPT2sIRFaozIXNOefNObIMHBFoqvK+6nVXbNsRS21NuEJSROTuxuslUx1Rrd+7EQJG5J2tTPW6I4lDzG8DUx5JVmWHa7VMz926qMA8Av27fz3TdXeKCpn20r6hOYoO8a2jlxbKKiSzPvIuIBg0p1BF7cZJJbIm0/vxrAAxQoAygrxnG3avu0DBhIXI8Cixkjrp006YooL/ANM+IXOe72gMhzke6QGUnJCh25V/p68p5xYyYHmlsp2eHA6i+Zu6AB2xaSmb5+hpVUH2fegBHfBO51IfhMvMRux+aNn/kwTlaSVjX7MFh+ktIdMCv1sx2WGC9mHYRlo/KYFUd/t73uYay8Si8dU/VZuEN/J6Fv7jsWZFbG8dZxfN/Hskr2xUwV0U709djFFk5RUC3PABDHDU5Mn0BYpQYkQK/wsY3m6DJz784jMSQhMw2fzkaME3QRdgkS0rQyKm21yoDcrY3cV3OpiEp9n/aCkaF3v7OHGcmyLebm5mi1WuR7wIg8o88tuPpqlg4sjH5ea+nnOGZGvjwwAjAz/SquufrnOXzoe5mafNkFt6maihp4eiXNcAx1JElynidO/wwAx479IK5JlT7bMRaxfvm6kSzbMc31xEX31YvjWY1nBEY8z+MFL3gBH/nIR0Y/k1LykY98hBe/+MUX/EwURVjW/sPYhpF4an38V3s0TAlebKosGibahQo8M1EPTN6/UDpNsBeMJD74mQRTZSKEj+fkCAGnGpdyb/MaBkIHCcteR1iSuVADj3LnzGg/tUQHbLsywfRCj9TWwakS68lgUPV1bxQF5R5mpFbfIqMYVQecsx0CaWjiIh2laSoy4GC4TH0ERnSwr0qXybjPjKWD08DzaWQ5wu6jrAyhxkxMN9gednIfMSO2sEfMSDU3q21vgh46erw6vZsdR0+MzVyRm8aDroSBH1PYfXqhD0qijFncbj3HNgZfsUp46frHmI11wDWVmDRzi7KzTa2vdS3rgRi9S6uzpidJU2/civv4xgbclsZm33EYFF0qMibII6KwyhkDRupZjdtMWueD8x5ZJRq1sK+lMbX6FmntvzNtnEcT1yfHHjEjLSPmzJOc0nZHzrtxkBN5LsoZa0ZSI45eN8Z6c26fyY4+11Js46x9HFecYji2UDRTfQ3VRO/5WPMcJ6fXOW6qWFqdR3nPn/4k//jP/jdzvW1cKyczYMTuNNkqBTPZuAkcwHHTY0n2VtgxjFjQPULp9Tj70t+jMO+A7aRs7OyM9CLSkyDgePM4tbCGbT9FUwS0M31tslly/SP34zX0uxpHTeYb89Tc2qiiRvRyPOM0e4lhBh8UEVlqjPpMmsap6vdVdG32TmF26VPaPmLWWNjv8dJpOPbINGxICth2Fc/Vupm9jeGybAxGrMg8+1tv5YXN6ujnC9M6vTF4ChipfgHNyFPHzMxruOSSf4MQ9gV/b9shYaDT05XKhcGI507hOHVAURRdarXLWTzw5i/r+F9s+L5eWGbPoKImu+i+enE8x/GM0zQ/+IM/yC//8i/z67/+6zz00EN83/d9H4PBgLe+VSu03/KWt/DDP/zDo+3f+MY38gu/8Av8zu/8DqdPn+ZDH/oQP/7jP84b3/jGESj56xrDNE1uzMmGjIhA0DRLvCEYUQhKG+SeO5Y7ikqSwzDkCB/fKPV3qWGh6JrWcrbQk92sqa2sbT852k/DgBHhN1CRYNasmCuRcXMNzf/xSRkzI9Vqm1wMsM0xH/fcERhR5RiMhNJnM3ySFxlvhvnM9JABDvf0xBuVTWQlpZkPECbQ1ssqXhmglDY+c8ws3i/3gBETqOu5Ptctd3LUOC1QGdtmsm9kkjQx3WEVtIM+CofUr+EmD1DtmX3XclqlDj5pmWDLDDvRIszMCDOtwaPkZULdnHvHszAfZ3Var+rOGxq9FfW0c6womMhNA0LboV+OhZZRWBvpRo6mAS8x5/+BBYeiHoNt+tKkMfPzp8iDe5hb1K3XY8+nUGNmpGrSHGVS0HUtUuN1stOwKRwbDDD33ISs0PdjN9LvyGzQYb43dPTcRB5/JYF19+g8t5A0DZCoJyCkYsLJ+fEX/xpvmNOGWs32WTxZYE/p1antFGRGOO0NFngsn2A6HV87wFFT7lt2V1g3KZ2gv8Rg+m4qE58fbecLeOKJB9j4+Mf1fTOmeMdbx5moTmDbBU8dvUxfW9mEb//wH+JVNWiM4wae53G4cRjb6EasXk7gajByONMsxtrkKrumKWJdCLaX+7iGXbS39i9mbMOA5Z4+ZrcYpi8tbCGw7RpqzxfYcapjZsRUrSil9jEjQz1u7eUv3wdGLp3T7+jgGWhGnulotbSN/dCn5KlDCDGqqAG49MS//YLg5pmMYcfdZ8KMXKhb78VxcTyT8Ywh7Jvf/GY2Nzf5t//237K2tsZ1113Hn/3Zn41ErWfPnt3HhPzYj/0YQgh+7Md+jOXlZWZmZnjjG9/IT/7kT37lruJZjmGaRiTjst7haEjBJtrREXR6YRh4h6O0CmrxcGIXOEgck49uqxoCRW8IRuhQJktMmLlifvvsaD92qvcv/DrZwGbSuHGG8Qa542CZfh9V5bNtwIgjSixLUtYfxfQI43HXJcwMGClShGELQhmw7ba5z9JujvOpflaeVXAw1hPvbnkQt7LDZNTBCvXPamUFpSSJnZI6Ea55rgPGwWx4T5r5BufQzMiw5w3Aed/oIZKEvNABJrUhtF3OBLuUboNK92NM9EzVUjNgwuhWCgOskvRR4OsxWAY5eJyHD0zhx128PCdzXc66MVflFc5MaCama97sZtzHlgdxKjtMGv1C5rgUBlgVlk3mBZyu6VX9LbmLheBcRfJo3eLqWg4GBNWSAY5Z2Q+btEV+QLGHGXGtPsgJyrSk6wpi1yfMM7YmQty0MlqWa82IYZ1S/Y5MVzbxz+pgbJfrDP7u+/EfPEXv9/Tz2ELRMGDEUlCPoWErknSVSWOX7pg0jnvgAOXWFpYrR2DEHyzwaDHBDUmKUCXKBK6jsV7ly94qqxWB2pU4eZ3S0+B12EvHsxSPf/rDWGeehBuup4Nmpo63jrNSWWH7AszIoND7LluKKx55mE0Bee5RqhDLsjjSPMI9vjYCE72citcgVbCYapBQzLV5yflFjhU2f/ITnwMFkyc1cHC3IHRd4txUS5nvQdJP8KsRbWMkNxSqo6DMqjhBDyFcLMvH3ZOmAd2Ze28ViYjAnpoiuPIKXpiXuiezgOsWG6ygwUiZS9KBBkxfqJrm2YzLLvsJlpa+6wtW3ABUa5fR7d3L7OzrmJh44VfkuM/Ga2Rc1nsRjFwcz248Kz7t+7//+/n+7//+C/7uox/96P4DOA5vf/vbefvb3/5sDvVXOgJL4KBGYKSxZ9VULwAH+mo8ueRPWfQocipDMCI8QltP3oWy6BPiIFHVBDWA88k1PPa+f0djOqaaxSx0x6sOlbT1LvwG+cAhlDYSqEQbZJ6Ha5xbayogNQFEmVW1qDwGqU4hPe65XJnsSdMooyswgapj6wl3IdX0r2elHMj0sXeLRbzqNtOb21hGL+IrjyJ36Lp9sEocc+wMSS4zXMsjdUvApWnEa223ocVxJq01LPW1kxTP+G5EtuB40eTRmbsQMsKP7qTV0wDJrYfUTFmvbj9nk0vdnG5oPGarks16BVTCVJSw2nQ578YsUmXb6EekZWFJSSOOaOIha6eZMcZYmeNQxWMAxKZR3LmK0R2YwPu5mQhEE1kfr76raYxj7O9d45A6CEJyLGYMGJHuDhRLFJmiXWFUbdSpB7SssYGF6yYkBnT2Cn0Ok9U1GrF+VlaxyebuFkeuuAzQZfNVFNV87GnTHEDdAE7H+JQ4vR0KwD54AO69F3w1StO48SwbaZUit5limy1mQSmOmrJi2VuhqMyQVzbwonkyR7/PQ2jpC9h0Ig6a8vHIjqi5NeYqc/i+f0FmJDambmULchOn4riBb6q+jjSOYAUf1ceJSgKngRvNEUqfjJL2w5KXpGP/nemlGotX5aSAvQ2NWWcERoI4Jw3hiXP/ksGpjyNO/iEALdODKksKyizECXqjztZDK/TcpGn2pmhAp2lqL3sZwrKY9S1+6coj2AIO1PQzS6OC7raeAyxb4Fe+cikK2w6pP8Wd9anj6JF/ShgucXDxO75ix/WehQtrerGS5uJ4juN53Zvmj//zT+Im8QWZEbNQZsAYjGQjsaYx70IRxJrVQPgElulLQwUQ1L0eV133YTJXsJbrZlmVdsgVu2ew9nbbTfQKU9geeVIFaao34g1S3ycwWoeq8omHNKzxY3BCXWlUCMGpPWmaXlDFa5kqhGzcYAtgPj+OUgJLSE4UOlDtFgfxKxvMWttYRjfgSJtK1kCZ/L077NgrfHKzekw9fR3NKAJjFx+7Hg8VDT7efA2pSXGobIBn7lvkCBaLCru1LkH0KfxMUk1NKioMmRiCEaUQIkChyJQkGi5wHZfcsUEkTMf6QS17KY809LGCRAeHRjLAVYJpy8NvrDFZ6N9ntotj7NAjA0bW/RTFWA/0xKR+JqoyfieqWTICI1bF6E/8gEw5ozRNPmxmWApWHUlsBKvdepOyMgQjCtdNiYxmpKsMGGmco6WaKKH76jy0+gjv/a3fGx3/RvYH+7ldhd/W/3YNGJHxLlJAZky8lA+F3yb3dxDK5lD7AP3cZxZ9nlMqZ8GghLy3TuB2Sev6ncpM12ZhBJ++UOzUS+JQg5HYiTnWOmYqMPwLakZS6YESTKX/GPfQm/Tn4gahsaE/0jyC5QzAiRDA45sQdrRG4rHgPFPRQVZtyb0HbP7BT93Cm3/0hVSn9bNxtgV1A0QsBX4aA4pB8lmUKhgY6/UhM5IMCqQRb4/AiLs/TbM3RQMQHruSye8am4S9YbbFN8y08AIbx9f73Tyrv/eVhvcFq1T+qkYYLnL0yNues2h17/CfhfHZUDNysZLm4ni243kNRpJ+FzeJIdmvGdn778Ee44TcLNDc1B+ZXoSp0XvsASM9NDV9YvIUtl0S2QGpHFYVwHV9varu1PTPZNKhMCLYWBxAqmGaZpPM8/AZgpGAxIARYezmHc/YaTsO5xyH0ICRJw8eZWJRT7BTxRiMuNKhVUxQmgZ7R9UQjCxRlX1qIh5pRkosatkESA8hwbM0KLFUfQRGEiMCrvUEwmhvel6NX7RO8CeTb6IwKY5dsYxrqpZiGxZzn9R2CPofY8KU9Hq1jMBtjsBIKkGYLsM9UYyqP7BNBY6ImDNCzuWg4CHTP2Zodjbb3SVUHlOOhVfbYMaApdK2UYFmIKJQA4Re5tE27rSPqhLH6HAKd7zSraYxrgEjsjJAGNdPy/XxDRjpezt4BjSccxSJO9QxuJSBBh2OmyKEIrGqZMomMS62U7VVKkEAlgYHj5x+kK0nx+WVr7ZK/vQNr+fz119H6nnMdxT2jgBpY2f6elS8Qz+AyDiEykCBgHhWB+YbOgfpZc7Ia+QmaeGgK2lksksz2CWp6xSiNPfDMiDNt6BdScdgxI453tTA4enMiAFnpYffX6Q2eCGT4vW4gzniqEFgmJGjjaPmker38E/u6xIYMPJocJpmMsO9XkF+qEK1adJ2iS5Ft3cEVSOmDcoCLx9g+z2U8QlJDNvRMq63aZSPTAyHIkvvKWmavUJWEBz/jf9DcOl+u3bQeo1hRc3GHjDytTCGdu7PKE2TXkzTXBzPbTyvwUhmlXhRgkDfiOoePdyovFftZUb03z1rBmWCfnWgA7fAHYGRLsY91YlZWbmUjWSeRI4p+jm7BcBDB/Wkq9IOsdIAJZ++Ggtwyxhb5qS+hyf1Sr+qfBIBdZHgpsZXwtarxMcDHykEtXI42XYQxiitUY5LoyfLSSwsSqXpVNuseneKRSppTIV0xIykCurpBFYZ4ucWrllNCtGiMEZvmZnow4496sm+6U0yGySsmzb1tlTcOf0xzs79GqDTNAdTlyADJz9Hq6sDU6WVkLtTI/fVVCmw9HW296y6ffOcbJkwnxpPmMDhQZNSyV2XSlZw8+kHqeAz4Qrc6iZTeyrJi4p+Bus1M3mmirtzfe/ukMWow3B/z4qzkqUjZgS7ZD7Sk7Xv6vPfUZLULamZMuxVV42YEQsHDDDx3ASUILJCeub9qrl9QifFrblY6Gez2dugsgcMz8sJ4mqdRy+7jI/c9ipmoir2jsBNJhBYKJWjsj79EHquBkrDj9eu0XT/zb3L6GQOi+iAfpNB2LK3iq0kLb9NWjO+Pm5knre57wI6lT5RRYPt2Ik53toLRvY4l5o+MKWysfLxu99ceRmREa8CHGpo63nb18fcHLiEbb3P86KPQNC1FDOmj5RSkiTRpej2tqC+pp9BYxDhFgO8+hi8ZbnWzwyZkXRQoMphyscY4XnDapodlJL7mBHHaVzQ32M4qsYMb/NJA0aaXznx6l/nGGpGyrJPUfS/xNZ6DIHLRQHrxfFsx/MajDzcP4VjrMJrlrVPeDlmRryRm2pu7K2V9FFSB6BgWJYrBb7Q/+6a0taaHXH60RvpUidV4wm5qB2ntBzuWjT54DInKjSomL3qG7muYlM1k0Dm+7jGxjyQPlIk/G3vPiZlRJqGeLkOOqcC06HWpFR8q41lhKa+8qgZerpe6uA6BCMAuQxYd6Y4tL1DZoFl+plEQJDXEGWFMHVGYCQuK2QyJRdQmkZ59q5CDMGIO8Elbp9dV19zK1ecrq2wZXrzxA4sJYJaZNxZ+3oCa9QTtr2ZkftqKkuE0IGvawK8LRW+UKAUSqYsGj3BZjXk3ta4kuC1D5+lmiWEyqMqBFUvo6JcPFNhkVcm+ND013Hv9LUAOEXGz5Lxk8R8XhbMRWa/ll4lWrLEViX2EIwAi6kOip5jmgsKRe641ITeZttlxIwEaux06boJVhEQu54RSMOkb1IP9Rqu1PejG3dGYCRD4WLzqnvOUhkM6DUa9BdfRbczM9KLSKN36IXQcfT9GoKR3kGf0lIs5DO4/UleyQf5B/KX+UZjklf2VkgdmHC7pIYZYU+zRNBpmr7X3seMXNLS5lye5+0DI4lhB6US2NkeMLL8UpJBE9/XJxY6ITV/dlTeW+ZV/L7WNPVMLXfHUsyaKpUs2zINIC3sXVi4915e//rXc9399+PmA7zGGIyUBoy0hmmaKEfJsf5EP4uh86wkz9sjAAPgOl889VE1zMjmuSEY+dpgRhynhm3rRUCWbX5Znxlu519kRi6OZzme12DE8T0s098ktAWZA7/y6gZ/dn2FuhJYKCQWsRFepvvAiDGvyjWV7UhJYA07meqJuMkAT0p61EhkfXTc0gk5N3sDjzaWwASyKB2vyOqWoJbpfaWejzBaC0cFWCJGCLDJ6XWn8HL9u1OesZQ3vUQWnNMIzMoWwbHo0L7f7wUju+UBuqHDTNznSUufuyp9uqKkXtRRRZVWMg6mkXLIZcpgj1ZPbKc6r4KuqDlZdsGs+uu5YtcbsOXpgCvNUvuokdtMdPS+W5WIFX9mxIxEsgvmfNqmn4iroFuv0YxzHCE4mOmT2Gy1RuLV687uMrOpgU9iWItGOo2vfALjthsHHg/XT9Kv6OdyZf1BNlD8X3KmlcX8QH8uNykUtyiw7Zy9koCFXAdQz7BDm0gSuzpiRnZcQWLs38OhcxcajFDuByMtV98Mr5LiS/1skiyman6/alIl82KG2z70YSZ2dpBOwB3hq4g2tR6pjNsA9EPBjqVZMWVopC3b49SEfh8Wo0VCWfBq8We468YzpLdKr6KoeSlFsINCIp2IvcO3IHdipCmJT5xkzIzYNrY9BmqbxrvHdzJcNdYROFmL6cHRETMCUAsXR7bwc8kEQtlk3i7hQN+HrqWYre9P0fj+PMJysZKEa2dnaZw/r8FIbZxaUMVTmZF8T2mvESxbLo7TAjTQyfcIWIe6oi80hmma3KR5v1bSNLBXN/KlUzVKlSMwcjFNc3E82/H8BiNBMAqgnitYnnRYnXT47KUBp+dcGiYADFM1iW94fhmAASPDjr1uUeCblEnblPO21IBAlXSpkZo0TWrroHPmwC30qSACvfqKsy6JBf/1Mp+HJ20qiVH3+2aCUwK7dFHDZnVC0e7M45pJ/4zRbgSGsTlsn0KI8cr26vb1CAVzmZ5gi71gpDhIr2Kz6Tb5TVeb18l8klRZ1IsqRVljwjh8lqpECY9CpQyGVvAypbGbjTQjW94kl2RdbKN9qeSa2YncsdlW5AiWsiVy9whTRkMzFQ5Y8WdHmpGutYkQpkusZaztS9hqTTKXu4R2jbnEbFszVShS8d13rjIwfVG2jcNrpXMJblEhNMxIbCos8PR9O1E5xasnHkYoyeXSZr479pQA8PIcx93fOGy21Hly1wTnTRSR3aJh0mNd1yYepmbkOFC5XgJFQMcL6Rmw0TA28G7QISz0symLfMSMnDLvojNzBbGT8cqP/DkT2ytIYdNd1uxOEen3rxfClmWMzAwzslF63NHUgfyYXMJO9HtXbJrtuit0Z8CyFCBIyJHufjASAL65jtRKCd2AuYoOWk5e4Jr7oxQ8OdAgp+am+HJ/gLq0WBoxIwCuv4jlbVGpxVxnNBxx83FmBofoC0UpYKaun2Mc62sIg0W8gwcBGHzmdl3GrlK8+jh4irIN6L40oAWswylPu4XqMUrV5Nv7rOCdL5MZGf3/awmMjLxGvjQYyfLdkfvq0ETu4rg4nul4XoORxJ9EmcBne4rYGy97P3h9lZbpxTIUsWauDghK+qhyCEZMkCyykWZk17iuTpR9QjFkRjQYCbvaRKqoHyWSPpavJ7wk7fHRWYffOuLxa5f6hJEOdJlZQVrSI1cCIcYiwV53ZsSMPGk8QFxlGuyJNhbjMtDrtm7hvzx2KbP5MJf/VDBi8QutWzlnhIUymwLlE5Q1SlmhmWmGIpc5CM8wI8YkTsUsdPuIdJymCUuFa6z0/Sw3beQUSH1OkQ0LnKSsfj9emYGlaNgZbXuSqokT6/46wjAjPZMC8KViqzXJgrNAxWmMBKzDcXW75KB0yc19OlfT96O6dS2Wckf9cWLPlEibv5t0+QfTd/G2lU9wQtk0kxBPjcFHsFcvYsYkOnBZJthtIBlYkzQNK9H3bRKjGXHUmEZy3QRVBuwElREz0pQ68Dt+h3qmA4EtLSro399jRLHWxGG2pqo4Zck1d3+OS04/MmJPUlPZ1QthQ+h/K9M3cTV3+FT1AQAW1DQimgFpIzV+QfZWaZuODnZWZ1dElM5+p9aqcgmLcSXN0WBxVD3iFjmOq+9PAeyYEuRmkOGVOkD1p7Ux25KcorJHi6XcBYSQXHb9R3jNtAaVWetxanmDjqWf1zBNkyTaMyUIF/EO66aAg0/pfjf9wN6nGXFKfXHNPWmaofBcqXFKaShizbPtfZqRLzdNMxxfScOzv+7xTJiRoVOr501ddF+9OJ71eF6DkbPOIqVx5VSBIPLHt2OraWMZEeCQGcmMFbwqx2kaYSY1P09HaZptpdmHqXJAIBRdVR1pRi4/9Ukq0RqWsFnIA6RhRvJ0wHJoKk98QRjpSTU1K0ir9MkUWGI8iUZREzeXZMCaeZS2GopMk33MSCIVoYK2o4NUIcf9I4Zg5BG7QWRWwyqfREkPR4YoGdA03XtzVaK9P3Kioe+HTJiLOiNmZNObYGBNYbAbdpHgihlzv3SQim3BnJxjercNQNBM6TBJc+iSKnLO19bApLwiwwiFpeL87AKzJ76ZilOnWkItGwOSm3dKmm4F24CRR1v6826ir7eaD5kRs0I2zEiDDip3cESII7TfSEt1RvsNkmRUSTMcTVufu3KHaRpFxgRBoM8n95yRZgSgNM/OcxNk6bMd2CMwMpXq+174HaZTn9Kewiu9EdA4jWS76COEhVzQaZkwUVx/xz3UzDaR6QTbDwVrqoNCjTQj5zOX0+Ihlt0NbCzC7SvwojmEEsgyRcU7dEw2xUmbrNttsPYDvQBnlG6K7ZgjYrwKdtIU1+hUJDaGsKLhpziFYf9aj7JdPYuFxdTmOHAntkZB3eQ81qp+bnHzcWw3pWvpHQ0FrMM0TRAcxDtiwMhnPgNAu+7i1cadZj2pn09zT5pm2Jdmr7HZ3oqa/WmaL94T62lg5GtEMwLjdMvQzOyLjVElzUXx6sXxHMbzGozYvkdhzMNkIIh8HQgDs8LPQv33wFDTo7400h/x38PyTj9PCYyAdVvqyXc67xEIQVe1UEa9P91ZZ3rzLgAuz2z6psS0SAasmLbkbVdQMZ18h8yIkD6pVFh7vCakdHEzxRnXRRruwTLHsUiwGNPsidQr7W2jTSjZz4x0Q4tNIRhYWjgrs0kc5ZF7Bar0qQ/BCCVCCHJV0DeLIFvGzMRtSA0YcSfZcpYY3q5SZtTtV3JsucKc0dhEDszLCjM7GnQFkynb1vRIL9K1+6xNbCKsocGWycsXkPg+D1TahLa+d2+9f4CX6EB/sltSdasEJvCvV0NWgvEzr2T657FJa+GOwYjMbYRVGfXgaZmGgADVJHkaM9II9b4yX9+IDSRp2SL3XRoqQrlilKYBkELfENdLKIuAxO2NgO6hxIBEr82tPRvbXsST3ihNs4VibaBZgXBCO3JWowzXL6ibCqqB0ar0QlhWOyiX0Tf8iQTI1/lsTbMjYftSvL7u4yNNVVAyq6/DyVqsM7724fCsMTOSOAmH4nEqy0mSkYDVcxokcug0u4qdm+Z/7oBHGtrav7FsjfoJdU0pM+0+fuSgREnSOIPtRnSGYMRoRuJkmKY5iGuYEdnRoDFelAhrnH4Jlf4+DgWsaVSMfl+W8ej4YzCyP03zpZiR2tOYka8dMPJMmJF05DFyEYxcHM9+PK/BiBf4ZEZdX/oOsQEjL105xfxuge/oyXVgtinMSvEWWeW41MHZkhocuHlOKHQgX5daoe+nCl/Z5GZbW2XYMscqTgNwpLDYCHXaxIojVgNTPeAKhEkRDZkRlE+iFK7Y6+WgcAvJE0a8etAKzE/hHcfewq8ufsNoy1wpLOmx4/QpKVE0OZcdZjWdo10eoBdaRD70DTUv8yk85RE5GUifWqn3XZjj57IgMmkat4wJy4x6pJmYDW+STefAyLozESW2czm33jPDRKG3iW3BrKoyu6krUsLJlC17LF7t2H06wdaomiY2/HrViHkf8NtUTEO9m84X5J4OpJe1M6QFvjnPgR9yT3NcZVNNNTMzcCxq5AiTpmnQReYWQoS4YmhxPwZztWTsvmoV+l5U/dg8I1N5gyIrKgy8gNmiC66FtGzE0MJ/aB7nphSlh1VsUWADiuOm30/hd3hlZPNrD72Fa5PjeMYkeQvJoK3t/Bd8DUacUuJWS6omF9O3xmBkgx57qoK5u7OBoOCuivYbqfQO4xswUk7m4Pj4LcPSZQHJHlO+4fCE2MeMHOqMKXknirDN96XqT3K48nUACGuVJDL+Lm6fB3zd3NEdQHa6i1KKbdlECZcTkdaARNXzKCfDcmO6lqIZugRD3Ycp6w2CRfwjR/adn1w0i4i+Bhc+Ma7KaIw0IznCGn5/dHM52GN8lm09pbT3i4MRbXI2/n/4PAUj2cW+NBfHV2A8r8GI64UUZrLPQwdpGsm9uPxzXvf5NhWzuu5hLK1tPdndKmvcLE03XtPf3ilTAiNC3Cg1wMijAA8Hy6x+XUOjd5o2u3aGhWC1eojEb9GbvJVVk6YpLUFU0av+3BlO+D4DpfD3MCMChQWccnUwnDf+JucDxS8cejP//vj3URhaulCADEmtgi2nDQh+LXkj/zG6DSlTeqFF4lh0TZpmyIxEdoySPrVSg4LCmIGVdjbSjDiy5PYrr+VND/0lALtugy378Oj3XQdi6fJw9QShEfx2XIGLzeWbpjHbZMqGMzUSrw5EQt/fASM0jM2+GkYjs+4zAiPrgYWyBEESM727zdqwWY8SpI7L56bHQXPYSyh2BC8Jn0AO90sHmdlgVRjqcqvZmMqvJ9EIjEilwabnbINSdF1BR6SsI8lzl8gLmFHdkUGHbdiqYem46ybkRYCV6MBXIcdX+hl2/V12hGI+q/Ca7HoAIiQxIDYeQsmSKWbhitfhHr8N+9jr8Q0I7bv6fU0qLoUjkKZZoMwsslRXq5yzd8gp8WXIxMZr9LM48jnUN7+JeeML05aK8AJOolVg0pQRx07M4uo4ZWgPopHpmWVVmbZvBcC3FL2eaR7o9emmLU7bOsANPrdGv5QkSlA685yMtQFau6FB15AZGVbSKKX2gZGhZmQ4nFlz/TtHGfqI1OntMT0rsPawW0PgMexPk6ZrFMWYEfpSaRrLtkZsiBfYuN5fb+PPr+R4Js3yLvaluTi+EuN5DUYsW+s4lCPIfJfElEHOFDu8vPdJ5o0IryeGrIBJFciAmvFnsEsNRoQq8I1Go200I3law1cOQurJsDR6iZ2pCTYDTS1HwRKfeeHbiVqXsxGOH0e3NY0UgqoJzkKF9NR4xQ+mkzBixIzMGBZhmI4vLId148RpC0hlE6so2XB3yAX86mu/id97w1tJ7IheKMidEhsLoSxU3sJRPpGVoGRA1TAjyu9x6JX/L8LfYWDm3sg/yA99/w+x4OwgpEQJiy33CB2T70hcl+3zJR+avQ3LCEjPmUB5oNQAKphIWPHmxu6rpEhLcjb8r6SuIjK3ZghGdnyLiknTnKuZwNNvo6ItVkw1iyU9vALu2eM/Us004Iwcm7rQz8lVGT4JRS4QojJq2BSm43vdjPsjj5H2We2tYdkZFSLOqx5vm/xTMqDMBf2gwqTUx7EKiSue6m2RkJc+VqKBX12kJLF+nwJvwD+3+/zKoV1yw45tmEqamfYK5e4T+jOXfhPB1d+Ctfh3AcgoGAyZkEYLAFnXV6JyC8+AkUZeYcXSQdju6/clq51n7SW/x3xFn892WeC4Y/HzcNSsnGZufGrkgInlsaZG9brYlgYDQlTp7NrmMy6B6b0T2wPKrMkjtk43xfdtsXW+y2IkmZcnuCrS97Xd0B2tLTfe5zGS5zsjrUcQzOMsLCD2lAgHE/r7l/XmsY2xYJ3uHjv4HMsZ66hy4ynim2qaweDUvut1nS8ORmCsG/laMTwbjiEzkmXro3TWFxqjvjQXreAvjucwntdgRBh3TxXYpI7NwPSamMzbXBp8nMNmFV0qi1TZlNa4C25lWNprmBHhpCPKtk0V1844GlyFh48lHR6fc3j7t13B77z6DbQnJuj72ygUlnCRtodIz5La49VopzFJ6TnceLbPNfEMtpokUopgX5pG0KHBuqGhJ8ykv7Wnu/BKqH/nCMGgnOJFj+2y6e6yFgpSz6V0XNphSeoKbt1d4Pcf+Wm+efMbABtLefQoUdKnYsAXbo/K9OOI0h0xH6Wtf/cbX/93qEc6CHfs2REYybyANBkySPr8z5ueLw13CulIgmrOPbWTI8OzotBB57PHd4j9nGTIYBTa+KzjuYSGGXm8ZWPJkpmdXX55qs9n3AcBsEqPZlRypmqROjq4VnOjL3AsMOm0uuohgDLDaEaMqDkbg5hGHo0ErEXSojS24pNsE3kOa4GekEUmeWj+MPNGb6JyicM4UAkkjpORFT5Wou9FTaQkkUNhGucN7F1+85DF//E/zT32GX7R2+LFB25HfMsOy1/3W7QXPsF29Fnyc7cj4vtpb9/Hx92HSKomddgw1SEGpKm8wg2P6QA/FTU4Z+21PAe5vYlyEtKGdkHddh1sb39ZLwqUXXK2epaHmw/j9bcpVscUfrQbY5k05vmHM2buNF1spUXVvJe/38/wpc2G6JI0bVQucd99P3/88QH/6/5v5mSimZFoUntWWG5Mb4/76rCSxvNmsCwfYVm4h5ZG51AL2wBkvTkQLQAmRI/QEiilSAcF1h6QNWyKN2RGklTvX5iWC18qTQN7wMjXUIoGxvoPKTOKov1Ft80uNsm7OL4C43ldh5V+8HMwcwNHNs/wjb/9K3SmD3PlE6do9dcYkPLq9Le5vnY57fkXMzd7Jx8VCUoJ/sOJeSbSOredv410ySMD7j1+gsfUC5HS4lu5kzBL+X0pgC2E3eexQwsoq8Gfvezr+PYHPsA1PEp/Zh2R17jpU3fTqQ6A60bn1m202PTrfOaqkq8X/5PT/XfRZ0+apizBttmmxZYRDlaNlmHbG69klkOLmyhxBAzkBKcnvo4wPs75xhiHdsIei8v/jreeewsfcO/h+EAbpFnSp6sE2AGBSVXlbpv77n0VNc8ZaUZKYwx29uAiM+U60GBg1+mYUunEDzmULXOlu07H6GfWjD6m6U1TOhmqcHigdglvMgJTK9WBf6sFiSdJzOo2LBX1QnH/4ZP4Z/W9aHtneOsnT+HKEqrgor0u/GSWVlSy2XS4Y/E0N+56hJkOyoljIxzjSqt6ICDPADFO0xTGUA2lqJDjmGqRxuFPITMf28mYYJu1YIG6bcpgcwmUVEzzQ3JJuQeMuHaBEJAXLqT6ntZESlpI8rTBVLjLD9z03yk9j+aNA0oheUvYxxLKFGqfIZn/FbY7Na7+YEZ427V07j/L6cXXEFSNF4wzCRI6QYEAVO7TMWCpER3mnL3FKNtnK5q/vE3ywy5qWt/7DUuw4JkmeUWAchKcZAqsmM/OfhaAK1eu4eMzr+eOf387ZSnpbFzPpS80VURJQGGErrabYJv0VHf5eiZkAQKevK7O5Z/rk2eSREosSpTKua/yGNT0yXm1hCW3yhuuOaCfmQELQXBgdD+9w0fITj2OCALqYg2UZkakMRmcsfpacJ2VlIXcB0aGzMhQMzIew3TalwYjtREz8rUFRizLx3UnyPNd0nRjj1Pt00d6sUnexfEVGM9bMKKUYmF3hX9x9hFetPYAtTwB7t+zhUWDPmcO3kC7dhlL2/fiWI/iWSc5O3eIc0px8+lHUIEWjPYJdXsuG2wbMkL21l4MjDh2u95iR5gvtg3YAyxxno3Zq/edX7fWJHCb/H31IQ6yxWfkJhtMjHrJOPGAotZgmwm2zEq3MMFtxR8fecXoUBwBvXISka3zhKxAZU9KKEx5y+p1bDFgxd5lxqSZOvE0g3ICzxZ4xgr+vIhotw+QhvsdWIcjG1rGWwE98/sch5NscMDuslvq1NiGP2RGppFBzjl1lMJymEsGgIU/GAeN2CvJTFVKpVA0MwV4QEHfhkvXHseVJREuamBz1D3KwXiSc7FNKyoAnw8vRMxe9lNUPvNSfZ6Oi/SH4tW2/llmIYQ9qqbJMn0/PZUBYqQZ8WrbZJG+R5NsU7guGPGmyCRhFjGwdWpG5JJYuiMxr2O2ywobmRtmxkrJZcnZ3kGmwl3mKqY8dc/9Pdc7wOWfXCNdgPxaSTFMPVVa1GP93iZhSN8LqPYXoAJRPaMKtO1tVpoaSFXySQbBDjuqx6SoY/k5Vizw/uII6296jIqlyIr5ETMS+geJylMI6XBg7VZ867OkVkwjO8qgMs9gdexFMqxUWbjkIO/eCfkx83Npp1AEvPjM32HjwCcBOHOkyqu+4Wr+5/IWP/ToeV4WrvHwI/8KgJ8S14N6lGqr4C++8xWj/Y88Rvw9YMSU91rH5vHlo/re9ubIizqOBbO2ZupS0/bBcvaCEc2MDE3PhkMpw+J9GWmamcP6PZg+WPsSW/7NG2GwRJ7v0us/RK122QW3UUqOmgx6F5mRi+M5jOctGBFC8JJzD3Kop1F97Pl89opruf3Ka/lm7zfoPlChXbmCSlrDCdqsxsfpWx9H2JoWVkLwqZm7efXnQlBw8xMrZseKxxuLPD6/yKGZo1za2eEeu0GBpj37fsiU2MZVOefVLI6w6E/VWJ+e33d+nVqdmyprHBT6i+6KiNSqEQClElSzkgJYp0UmTGO7SE+e6/44lXPOZFccIJIT/O3kL3h85iynw388vhdBg2/cvpY7bD2Z940AtJtOoWSNFt1RX5q20S9kQjEwaSVPQmGlSOHTQZ/Dju+Mu+wmJaERA5elY34/ZEZmsPxZTqGvfyFJgApu1KfZV3RqgsQvyR0d3MMSvv6he6lEA+BaumUX36R+/uDG2/itn/0ZFq+/hTuLAlA0Y33cbVPKXDWlzZntkJn71BRt/TPTCXEIRiKj9ZjY0WkD2x2DPGUM3ablJtighrKQXBJkEbFv7PMzSU/a1IaGrwbQpPkeMCJScqX4xXv/Pn+rdp777YLZWswN2Vnadsofbb0ay7H47ff9e9ZfL+DaFNkwqSQVcPlrlglkQmIFbLSmuW7ztTTiKfzqbwBtyhw0FWJTzQPiAM7TY5I6mBLeMDzIu9bOEViKl6Zz2K4WkVYmjhBtnUK6EfOPfQuvO17lD73/y4seu5eTp+5l/h3vwDt0iP47/w3Lw4Z61QnWB5JC2jhWiXRiZBmiUKNS+F3TgG7TMGEH60d43HJRKMJiDmmDX93veJsmWveylxnxLzmh/3G5EdZmLWQRkmUNnECnaUC7rworx9rTWXjYh8a2q1iWv8d7xJRgfxnMyMkXLzB/rElrtvIlt/2bNiYmXkS3dy+7O59kYf6bLrhNnu+gVAGIp4G6i+PieCbjeQtGAD5w5GaaSZ+16SXe9+2vprQdbFXwuu33sntPjfWZBa6e+QyLV/0Fax/7NgohsN3F0ec7XokT9XBKmNvYYOJEn/YLQ/7B4N/gz5S8oTXLSx/doMz6IydOhOCx8AjNbpv7J49yXfdJehMN1qf2ryq6lSp1Mc7be6pPKYx7Kg4ngsu5mzXWxSSgRY1uOUzTjPezXBkzIxKHl1VP88p4i7dUvm+0zWR1EWdLsObFICEWGU1ySjvjm+yPgpXiWi/S51XoV0bazogZ8aVC5mdoDprsTmlQMUzDeHkO3XgERqQBI30H7qtJru7bXClfyf8yJZfNoWA37rC0ZcCIJ8ncYY8XxQOHr+a2e+7QzyBva59yaXHpiiQyDq8D43HSiPV+ty1970Jf39PMcUkMS9ESxtOl8LAEWAZEDYx/iNXVAXRvk7zYifGBuWJDM1xGtyMUVMuMKNTBSeSS7T1gxDdpgqgMUMYNtyZSSgG59Ai7V/MIA2Rvk46TsewkdLIm0+jmdXlUYgFGe03Rz3C/92dp/uFdJATsNluoWHJ092qmD83Sps2BNegf+zZKZ46J+FHiesADqsPSkZNMrX1cn8P8QVIlSEtBNWthVTTjEYY6ZVe6fUDwD0+/gRtfeyNXRP+bvP0wM+4u9cuu5TEDFADyvEaUSeIioO4NNBiRFkqMPUB2DJjbzPTzWag0+Zmv+xkEguKuj0IN3Mp+MJIYEa4fLIx+1viGr6fc2Sa6qYCtz9DO9O+ytEolgKYBW2mU70vRACODMyEEnjs1SgMNh21/8d40w89OzFe/5HZ/E8fk5Et58uwvsbPzSZRSI7fdvWMoXnXdSSzLfdrvL46L48sdz2sB64eOvYhfu/L1nJk6TGnrwFOnx5Y1x6sWe3qSmtYTVOuaj2NJi8wdMxhC6JWTLY2wdSonMr4gmRPwgLVBSA1LOgyCvRqNGpYUbC/oXPVOfYL1Sf3vCWO41qmEKHus/fBFH2m0IaVyafqa8mjT0j+zpylM+qfrjY81FLAKIy7ciBcY5Dss76nckWGDXMDpcDxZH7Viqk7Gj/q/xSHWcYVPiSTKTJml5dA35xcWCrvc5dDZT44+f8ZE3yBNcHsJwdDOvBTDm8evHtXncFV2ORvlZVqbURhh8GCXgyZbkXgl+TBNU0IzV/hGvLpjOhNb0uP1d8WsTL0ImScMzDOpGGfTXbO9XxmDkdS4sjXQ+o4s80esiEIRGzBSjY1Vu7OnlNWImaeVZk2UE2AbQBUWxQiMkEuW1VgIO+zfspXXAYFNSYWc0jyOgxWPioTAOPum5ivq57qkNTHx2THpt2J7G67/Dhqlvg+dehMZm6Bf18EhGEAR3kZWuQHHdIeW0uYv79mm+/gZACYWjoxKjytJc5ymCY1AVCh6c3dgS8GNH1vCX7hUH39Nm9aVaXd0jf3E9HIy4Fg6Mfg2yoivS2GxZZ7PlgEjM57LrQdv5WUHX0ba1+ft+PvBw4XSNFYQMPUP/yFZVadjdnINRtJEA4Q6puHkoBhV0liWMTHc06H3qat62649763Nm80XYFkeabbOIDp1wW0u6kUujq/UeF6DkSulXhk5ewyeanTp5S2Wy1sIKLCM90gwcY5raJLtqaUfghHXdIJ1gpKO8fooMsH6wMYTDpZ0R+6uAO1KldhfZLeqUxrb1Uk2jOHUyZ6enDthgLdnZRjQRw7ZBeUgPbPCVnWEEpTODKl7gJiMvjteobR9m8SCwtDiH3/0Zj73sdaomgVg1xP878Muohzbfy+IiEJ6uEVCT/q4ls+u0Kvj4eg5QyMysModZrY+SRjrFfVw/9Wojx8lI62LW45p8k/OVVhJ13Cxef3ZBvWixDZknQYj+jOaGdEBJCwUzVzRNK6ju8Z1tG9AYLt1E58d5BiCBT8zzQu9EAWEQzBiOySOfm4NuhSlS6G8kXg1pxwZztWSAaBGducABgswYfrT1PMWjvG/D7KUQWh8aHLJzl4w4umAuGV8W1puDyGgdBxsWVJv+hwu7FEJd2p62oRKP78ux/T/zfMvtzRiayQ66A6q1VGPIKuhPysSCFN9XGlSEMNuvsmKDibBzDzXzlxL3a3T6s9huXp7359DmHTV6pW/Qr8FMioQjVcivBr5qgYjRTb25+iZOuzCVGBJJ8aquCijKclsZwRChszItOuwuvZHrK79EXFXH896SqO+ERjZw4wMRxRpI8ENaZiRSN//qnFhTfYwI5Zx9c32gJFhRc3o/1+GXuRrfdh2QKt5EwC7O5+84DajShr/ol7k4nhu43kNRpomQEbCRhhzrTo9OnmTU+kLqckSJxh7KdzSTIn88aQlrf1gxA4ku6Zjr7WdYZ0XCAQKl9jfz4zkdki7UkMB0nVYberJ7/KuniA7gce5iTFICkUfZVbeChdlFTgqByyqeRVpt7BEhU2ru68fCsBKaKGGDXYrh0iqLyRyxuez5Sp+9bhPkO/x1bAGpHmAI0sGpYtr+WxZ+y3C+yZyVwuFn/Xxy4ITp3VZrRy6mPY7hPE4iO8FI8q2+Y+Bvl9vOp9z24a2+s7JCaLuCIzEfknuDJkRRSNXTMghGNGpk9VmhT9+YRWUZM1U/nhCEeR9UIrCtunRwKsbwaUQJPYQjHRIioASd1TWm1EQGzBSTwbYdoElxs/DcfR1NEyn5ut2T2K7+ve1fo9uVV/XVA4JY3Do+Pr4K4X5va+fd+66BEVGayLkSGHhG48RFWvw61k6PdhVegUf2jHKURTb2yilqEdtfd6VYNh+BWEAoUhhMksJLEFprk9UjMC2p4/vTE/xnte+hw988wewIh/bVNO4TnMs5HRy7rsxwp7wQVUIb34b+do6qiwpjK8KQC8yrI3SgEA6MU7NHaVpcsdh27xrm7kG2NN2ykMP/WseeuhfE7VNqs4agxEps1Gb+r2akeGIIp2qXFP6d7nxUAlMfyHtMaLBiG3r88rzcYmz9xQw4nwZepHnw5icvAWAnS8ARkYeI95FZuTieG7jWYGRn//5n+fIkSMEQcDNN9/MHXfc8UW3b7fbvO1tb2NhYQHf97n00kt5//vf/6xO+Cs5pgxAiCybqqG563QZZA1iVSNUJbY/rhZoNgVyT15UWcY0q9ATqxNIdk0lilBQZWj6tR8cdMIagk0K26EXVMgtm16gA99VPR2QO77Dp1r+iLOpiy5qaCqlHAosWkJPtLWihrSbuIRsWB1izzfnpbdfCQWGGKHfuoFP3fRd+87nbFAQWxKvHKchfBGRmpRJkts4lse22A9Ghq6o9UKxuDtJJba54rG7920z0e3gZ2MAMgQjwlQs3OkK7gkgkPAPH9cr+YgYP0lZukCaJiy18VnTgJGeCXBREHDvUZ9G7w4cY9Ef2BKbgprxONlmGreajVxzE5NmatAhLX2UaZIHkImC2HQwbqSDUSVNrhxkOf7aBE6Cr2Iiq4owrP7ETptuVb8Hi7ki2dux1++TS4fTpl/LC2bu08dzPcIyZWa2wuF8zIwoRwfVeq7fz67TIDPgpmyASlPkYEDNgNgi1PfFrzijjr0iEfzM0iS/efVRCts0VKzpgGzH+h2yp6ZwbZe6VydLSmx3mJpq7hNy9q0e02+9CmyJPXUcmVxO2e0i98gr+gNzE03H5dKJ8Vr+GIzsYUaGf7fYRqkSpUpK8x4qxgBH25IrLMvDfUoprlKSKD4DwLLQoK3o6+vzTFti7TESm2sypoRfJE3z5VTSPB/G5KSuPttt346U+dN+P3ZfvciMXBzPbTxjMPK7v/u7/OAP/iBvf/vbueuuu7j22mt57Wtfy8bGhW2Dsyzj1a9+NWfOnOG9730vjzzyCL/8y7/M4uLiBbf/ao5WVU/qkbAJSh1s6vQoEr2KC50cIRRKWgzWLmdVPOWczYTllhKEwvYk21L/zAaGsrbI2y/s6oRVlOlrslOp0wuGAUdxJNE+GB3P4sFGlQfs4wA0aSNMmsbCpcwF06MUQR1pt/DwWLZ7FEb/MttrA7BcsUgsxXmnoBAbJLZeEVaHbqaBR5juz88LK0FgUSqByF0sYbE9YkZ0MI+N90cjVxzfOEgjcml1d/ftp9nvjZijXFgjMGIZV8emY/FLh/T5zsc66A2ICdKUegwv27maq6Mr9oARk6Yx7MeQ3YiMhsZSGTev/TkLrqBRa6OsnIbRUGyX0wjAMyAts8aakbjwQdT2VNIUpOaY9TQaeYxEskKe6Gc8rAyaZIfIDlBGxFrZ3aVnPD+WMiiwKIfeFX7MZ9euJ1M2Tb/D9dO6LDd3XaaskmbLp64EgWHBNgv97jRTDYoHYZU2WhtUzurzKzfWqHWNZsOzdEVK3UOatJGVwjWW4iWBQ25SeO5kA6vMsI25nDOtg7GUiiItsIxmxHWb+8y/sryPO1uhfkuAKnNEeJTOn52lNLFbKYgN+BuaCko7IZgMRmCksBx28oJBUdI3qcG6Gr83w/4xxZ5GhcMUje8vPE1ImSSrSJkihMuKa8y6eqYfVNnWNvJRjm3SNENwVRS9UYB9qtfIl1NJ83wYtdrluO4kZTmg0737ab9PU52mu8iMXBzPdTxjMPLTP/3TfM/3fA9vfetbueKKK3j3u99NpVLhPe95zwW3f8973sPOzg5/9Ed/xC233MKRI0d4+ctfzrXXXvucT/65joFxFoxxCAs9UdXoYSUCN2/jGcFimdTYefTVrHBw3+cLA0YcKbF8hbBgy7h6uoYZKZUiMn4WNVNmGvnhqLnabrUxAiMLsWS60ELFtieQfpU/rmnauWr1R5O0rVwEkhn0BF7LaxqMSJtz/tAKXTI10KvCldDCsgQfrLfZnXuYraY+xpUd08HUtqlHeuJ3rJK61IF+0orYKmr4uY1Esm0aAU75m5TCorRMeW4Oi+cfpxo7VOIxkwQQxMnINXbVnxg5sAoDaOquze1LHve0xq9iZFbEUsC/XPuHvOTJF40FrAUciCW+MIHYGLxFng+5JHHqhGuf48ZAsj5zJ9LKqEemvDfTE6ZfDM9hyIx0icoAYTdHzMiaYRWsMqdSpiNmZCCrpNlwZT12Yc0cF2V6k4RRMmJGjhQCEKypJjX6hGGbDz/5CgBeufRxAhMgc89lwVf4BiAHRl+RGg1Ny3T17YeVERiRB2ooFKfP/Sz50RghJVgCaWWEdRdlUkkihbLfp+z1R2CkMtUiGAZ7z8Oq6bRRnhQIOxuVwDpOHdcZ0x5FoZ9NeM0iyed/HYD4ni7ejO6jo5RFkmqAM6xGkW6E1/SxgyHPZ6OARyJ97b4lsItxysQywK8o+iMr8rFe5AunaMLwEIn5XmHAiKCkLPuke9I0uhuvZZ5h29yCp6Rpvgz31efDEMJiYuLFwNN1I0mywva2rsaq16/4qp/bxfG1NZ4RGMmyjDvvvJPbbrttvAPL4rbbbuPTn/70BT/zvve9jxe/+MW87W1vY25ujquuuop3vvOdlGV5we2/WkNKSZ61Aciw8Q01fOkjZzj05Dr17iM4pvEYA4/B2pWczbWnwZzJk+auSdOUJbavg+t6aTr2GjCSKRiYdNBkPyHMTL+bip4sdyt1+mZVP59IJnKzb0tQBhN8sLFJT3m4IsEyQd1VDlViJo1Z1xCMiDKmZ7rXVoqUuqkCWQkFISDdNgrFEwc1w3O1ASMIQc0IHHEKpkyqaVJEPDKYo5oLOiKiFBJl5cyJgswZpx4mUkmlfTteYWHLksZQPQrYqSRA38e+F1AYADMUscgpBxyb3zwyFs9GRnSYBgGquk0/HRtKhaXikr7eNi1jbGcIRgJELkmcBrK7zPaHfoTLTv4BykppDL1GyjmzDw0shFK4ZYpLQVL4CKuJaxiMdVOJHSZdHIsxGFFVslw/OzliRrZJXI/SMGBOUjAw1TSXZHp/H0ov4e+7v8uZ/iGe7C0hkLz84KdGnW5z12XOlQRDMGKqj4YpnqlIA8tBWKFjKqiYCyjm4Hz5/7H5DVAdaKBQ2jGVhkdpqq9EKpD9AbLXJTO9XOq1Kk0DXEVzcsQ2ZEk5qqQRwsa2a/v0E3lpGJPZWYrzd5A+9McAeI3r9D2RFkVmzOFc/dyknWBVXPyafvauAVgPG2O7adchN3oQGBuTKZWNvD9G7qv+FwYj1cqxEZij9EhM2+Is29FN8gzws50arqvv4RcyPrsoYB2PKZOq2dn5xL6fn3nyF1Eqo9W6mWbz+r+OU7s4vobGMwIjW1tblGXJ3Nx+Sm5ubo41U+L31PHEE0/w3ve+l7Isef/738+P//iP81/+y3/hP/yH//AFj5OmKd1ud9+fr/xQzNZ3sIbVBQL+9p//Ga/7b3fyug9+mIWNT2O5ZmU5cPCTDudT7UJ4pXWPPk9PB22nlLiBDuzrhfaOCKSghiBXEJlmdWGW0zK9W3oVPVHvVBv0DTMyHyuKXoxnqOvQm2THifhApYVrJaNGZC4ux7IqUwaMDAWsMm+P/EzqeU4j0SzFcmhRQZD7O3x69lN0Ar3sPzqQ+KVxdDVmVJlTMGUEAFPWgEfjOWq5YsvoRXxbUeu9nNykgoJCMZlkWEoigIrdYDIbA4syFyNmJPZ8It8wGgaU7M7q8y2cR/DEQwD0DeOT+RWS8FG6piGeUApfat0IQFz28IzQM/ICyCSRWcV7aZ/SAiUkdSOg3VSawveHYARF1QTXMg8QwsERpgTaAMhq3MMSY/fViCp5bqpETJXMBDvEnk9urq0Rj5uxzeWKBpodOSUO86GzL9c/r20z05gYdwK2bWbskqCqjb/cYWmvASPTff2sB3uYETHtUE7q56d8aKR6G+kkVOoecqgxSkH2+xTd7ogZaVQr1E1wVvXW6HyzpBhVsThOAyHEPpZAmvslPA97epr8/B3m+MZ/RtqUhb4m32uY84mxKg6uqXb2TOPIh/v6+DOeO9IeAFqnZaqHhl10L2R4NhzDSppK5ShN3yExubaBMp2v8x0tYB1pRmq4rv6eZiMwojUPwjBuFzUj4zExoUWs3d69o+eRJKusrPweAMeO/sBf27ldHF8746+8mkZKyezsLL/0S7/EC17wAt785jfzoz/6o7z73e/+gp9517veRbPZHP1ZWlr6gts+hzPj6zuvGa1Ar3roYd723t8AtNq/tG3wzao186kOVljz9AR2tXM3ALFfR6HBiGPAyGphqh0UVBBkSo2YkbCIqZtOrcPUTLtSo2eYkekkp9MVNE232KVce5r8ad3HFQm2Cepd9/9v78/D5TjLM3/889bee5/9aF8seZH3HduswcEQAiEJCWEYIA5DEoK/QDxZcAiQZRKYCcNkGX5hYgJkvkBM+A6QTEIcwCyJg/cFbLxbliVLOkc6W+9d6/v7432ruluLkbzJsuq+Ll22uqurq6pb/d51P/dzPz0KdKnoRbsUlxBU6McNerqcUQvi7L32aDJiG332luZoO+r91nQT6lrFkIZaWNtOwKRWRiZFl0f8aUoRmV9kY389BekQ6LjzUiyp9wfGttWFzYwPKSM9ClngWd/1WCmrfUvtqF2qKWJ0Yetexuz/wddr3+XBvroD8z2PZPFRGrozxQkDht0C7aiJp8s+nVQZ0XfjhkyQoUQKQUWHcyyak/pcB8d3cVMRS6Hn+qT2nv2eLq112whzQEbaskSs24rTYabjLNJzPQLHYU1rHyfvVwunGUUgBVv0P7Nvhudz57wqT54x+QDl8imYZpjtqG6EuCULKaLsPH0sDCNmrKcI+UiZpi5IaoNzqWhyGpt9ClWHRLc9G31IOm36Kyuk0xxr1SIl3a0Se4OFNxxSRtIFeVglSJJBh4s9O4vsqfeUOp02SSwS7cmpl/RxajJi6dKXF6tre39HkYMpxyLwB8qI5TURegJ1FOnWXK2MuIds61XKSLG4mbpl0U1nIkUDo6rKGVHkxzLLOHrWShp8ViptZeOGd1Eun6q2yT0jGQqFNRQKG5EyZnn5ZgAeH1JFxsZedIyPMMcLAUdFRiYnJzFNk/n5+ZHH5+fnmZ2dPeRrVq1axcknn4xpDrIWTjvtNObm5giC4JCvueaaa2g0GtmfXbt2Hc1hHhEMwwbboyQiTll6nHd97vPKVHm6pPvSCSLLAkffnQYeMlnIvB+noNpXY8shtBzsOMHyEhIBDa0qFKWghJqb1tHKSDFqU+spZaTt6DwS02JFqySVfpdWI6LWV9fFiNUU0zuLgv88U8bSZOTb1e9xX/FOOmZAYKhtq72ATtKgp5WRsYCMjLRtQWSBa/ZUm7FXB2BtVzLlp32g6hhbdkxdE5Oq6NNwqziGk7X1ziZjeDiEukxTiqA61C0zVVjHhD9YIJtmCU+TkU6lyL7q+OBDGBpNfn7rPgxjL/9j1XXIrlqYup5Lcs9O2rq0YIej35eG7pRIhFAdS0FCIlwiHWqVNNQxlvtqEVq0xniQU1jRhKUcwsvbdwNgarXDLakVcyGdW9PtgGmOlGnSEpPQPbTjLNJxC0jH4lfu/YesRBObJkks2YLa1//u/RgSg23Vxzl17EFKpa0IAZY2UVaMELdgZS3coTBJMLDtiEpXf5aFIstp0F05Iq4PrkdJd1fFZg+v4hBLXYbxIWq16K6sqL/HCbWiR0G34wbOwBMS9KKsk8bWisjIwjxMRlbNQhIiww6xfk0cWwjdzTRV0UZbnTNiaH+PE2oykikj1ogyYnkNTN2pNrgTP3yZppORkU3UbZMdM4pRlpfV5xCESyM5I6ZVycLcOh0V5iWE4KSTfgPLKo2cew6F8axU8+/0+3vZveeLQK6K5HjmcFRkxHEczj//fG644YbssSRJuOGGG7jkkksO+ZrLLruMRx55hCQZSPcPPfQQq1atwnEOPenSdV2q1erIn2cFtmBje4Hfu/nTuGHIg6ds5tTT93Ju7V5iywRXB2rFJVaKutvGb1OVTVz9Q98tlLASRUZ8ywQESChJVaYJJHS1MuJGXWra4NmyBz6Itp5j4vltwuU+NV1WWDDWIqUyct7rCUxdQvDNPg8VbmTBMmlbakE5d/8yHdpZG/FU38KLZeZRWSmYWEaf2JoEIfAiST2UjKXx6/pYWpbA8doUpaP4iethGW7W1juRVCiJBQIzJSOSSjj4bOvOzIgysmiVKGj1qed67K1MYmq/UEqWAM5t3k+TAghBtaOe7xVcgr0hPS272+FoPHhDqmPq2i4IgQgTLCkI9eIaL3v6fdQ1aDgVPs41WQaKIwVFbRS19VA8S6fXLumyQ6XTRRrWoEwjS9h64TU0aRhnka5X4Jyd93Ph/AMsl4eUBEmmjITaK/HjMz9gtjCH561WngxNRgoywjANcNQx9S0HgaRY8bMU2PZQN03k+cT1oSwaO/WM9DEKJrFUyoPwBUmzRa+prpeIY8quhRvqhd4cfBeDfoyhM0ZSEjKyMMtBCcqaVSpF0lkksdRr4tjGJqboGIy7aZmmj1GwQCfeOtpHsxAOAs/SYWsAVqGBbavXRlFLdcMcxsAaxz18HRNfLG6mZpn8y7lFdo+b0FPn1WnuI+zHWQKrZZaoVNVgymbrByP7i8KmPve8TDOMiTRvZPlGHt+ZqiIX5apIjmcMR12mufrqq7n22mv5m7/5G+6//37e9a530el0uPLKKwF429vexjXXXJNt/653vYulpSXe+9738tBDD/FP//RP/PEf/zHvfve7n7mzeAqQUYJcavIbP/wu9STioXUb+defeRHCANcNkY5AaIPfoqixWNGdI50OS3OrswjxTrGMFSeYXkxfL9AuUJBC+TSGyjROGFDXykjLGsyzSAmE1VvCXulT6+pob7sKicNPL5T5/UUlJydS0C3/OK/ctY5F06StF6Ct+5VaFevC/ERo4GJT1Qv+/qKJa3SJLeWbWNNTHo8x7e9Il7SWZRF6S5lvxHJsEtshFDGmNNheeZAJ+/rMM1KMJaUoJTSCkl0fISPLRjFTRnq2S2g6mWdjrKN++Mf8RcajJk0dGLdOk4i+59EtjtHXpRM7CugxKAm1hSaEjtpehDGOFIS2urZyRalEXtDHjFXJpi0qFPXdfc80KOjODTcoABKhc2VWNBkp9rtgGFg6xr0ji5SKqvMjjYefkIvEpsmd557DL1/zx3zqNT+XHaMiIwNVcLqwnzNKcxTtHo49geOMY+tzcrRPWPeJ4gAAeAdJREFUJDVD912HU9ftY9UZK5R7aWvvoEwTWd0RMuKV1THGVh/pSBI9p0f0IV5ZodvS3TORpOxZWDq1tcswGYkOKtMML8wGAzJiazU06S0NlJHIxhBw0kQBQ6fMJnYPYRmgiZ/ZEyOq2JRjZ+FZoJQRx03JSJMoahHH6vwPTF/tdnfoY63jOOOM2SaRJbjuJRW6+jt8/y1qAKQ5ZGCtVs4CoNm8J+vYAQh1XH6ujIyiXn8RYNDtPsbu3dcBsClXRXI8gzhqMvKmN72Jj33sY3zoQx/inHPO4e677+b666/PTK07d+5k797B0Kx169bxL//yL9x2222cddZZvOc97+G9730v73//+5+5s3gKkHFI0Y9Zc9G7ac6exe/82m+yKhncncmqi+GqhX63M8X8uFrgxrpt9m7flJGRfrWQlWl62kfhJQJvWBlJDayhT1UvKqEOT3N7PRACM4kJwyWK7b4qDQCRbYGQ/P1Em+lI7cPHBG8zrojYP0RGpK+OPdYLcWxvZ8VqUNHvt6cgKJk+iSYj67tqwav5aRut+io07AKRt5R11NiWgVlQi9+YLPG52S8zKe/IumkKUUIxUa+t2hOYwszIiBkndHBwdb5EGsZW0ItkqhKd7KsJsY2kzk8tvYrNDbUQ9D2PXmGMvpkqIwF9OSjVdHX6alfvlyDBkxCkqlMjJQEBNX2+U3Kes+Ud6nhsE1cTCjf0mDrzK6yc+yt0xx6kqVtEy366gOn3ikuYbprBoRb/qmiybccDADy8fhO7NqrhcghBImEjRqY8Xb7hu8jEpWD1sZ1xHGcSC7VvU5MRQ/uPfMshrjnEuFlWS89zaGgyEormCBmxKjoszAjpDQWGGT4kjQadlv6uJFB2LQzd+t2OC9m2w56R1CsybOY0GOTR2KsUGZG9ZWJLvSaKFLHeOOZgBkOzaYAknU8UGRSHSnlTVkwUDZKOTbeDZQ/KNKnyYVl1THN0Ou6gk0aVNOv6e9n1DB5Yr0ypqS/EdAeekXL5NISwCMPFbP/q/Rr6vXIyMgzbrlKtKr+TlCH12oWM1XNVJMczh6c0Ceqqq67iqquuOuRz3/nOdw567JJLLuHmm29+Km/1rEGYJmHUxGSK6897A4v1cTbOP5E9H1eLWJ66c9zjzdCZUT9OY90WnYUZvNAHB4xNBt3iOq43fplNwZcA1dZ7/5ll9hVM/tOtvUwZ8UIfJ44o9bt0tIHV6/XxCwXK/R5No48B1DrqDjZ2EgQhCfB9uwqRavW0EQinP0JG0sUunW774Pg3mZ33RkysbtgnSpWRrqQXdyl3nwDGlWEXaHmzdPvbmUxOB8BxLGKzCCxhGJLd1grIOrH2ZbhRjItFYnlMeKpleFzPRvH8MCMisRD4Vmp67bHIWHZs5wQqhXReVnn7/OuIAkUWFBlxsu4IJwwIEkDHefd0maSrk1LdIMFAZC3Xom1CBZB9zn+kzwPrEt4z8UfcbZ+n9m+a2GaIBGToUZh6EISkV3+QlqtaFQtWG2Qta8HtREWCgiI9QkAcGZhWwp996vdoBnX+y0/8J75/yjZWra+yOwhBgoPg3KrHSvIEl62+mWjuIhwjxLHHMa0JrFTtSQPhnAQCpZh13QJeT5uMhcAxI5apIjEQJOhYG4UaiJaNNEKafe3rioEI4laLTrcLlkGSgGsZ0FSLdEeWCPoRjmeNdtMcokxjG71sgqs1m5KRJRL9mlAbgdfXHUSgO2y0cTT1iAlpUe8kdPXwyAmxoumYTRJLDDPKhtRFcetJM0Z6PRUSWChuBKBuD1SoXmkcfLAK6t+IqcmjaZUwTZdy6RRa7R/SbN6D561GyiTLUbHzMs1BGB+/jGbzLkCpIoea4psjx1PFCTubRlgWizUVdFQv1AFY6w+MuXFRZFHwewqr2Dmrfgjr3RZGUiRq6BbVNTbbt/ws83IbO5qvAMBGUDnphySr72DJhMBOW3vVj+GEbtMEMHXdvOx36ZsxvuNQbSkSFLoDL8ZenWbpY2MlYFp95RmxB3fAQNZNE8UtfNPPyjS7Cwa24RPbKrhtTS8h8JeoNn+o9quJQru4joaznCkjnpAsGuo6LLgL9CX85UMbCHX6qhNHuFgEM+uZcJWEfnIrwUgSxlb6WYnGt+zMJFvRxslC6CNkwkWB+oFblGX2A/jay+B59Mer+NZAGQnigc8kMAcZIwAFfbftO4ropYGxSdLhRQ/5/OrXdrM62U1ZP+FblnJ3AknkYZWVutQoNrPzK02pzypLYI2K9ESJMFSkz++r947HJPPjkzwqJnB+sMy7qnWsWJVpAC4vF7ly/b/jWQFxpPw4jjNBzBiWbsGNdReKkXpGNBmhp/6ZxrZLLe4ghUlo1NVxD8WwRzWJqclAs63KHiJQ85GSdpu+9s4kmAghiJcUGQmcCu0ldX4qZySdS5MqIwMy4ph9/Eh9LwdlmmVi7RnxI6WyrK1YiL66RonRQ8oEX4ehCWky1hnkDNV0F5Ahx4n7KQlQ1z8Km08eeKbJSLGwAYAxe3B/JSzF1MbXxghDZFOALe2RqVTPUNdK+0aUWVa3uuetvQdheurVCGExPv6SLAgtR45nCicsGQFo6ATNDbo7YvVwe6EmAjIx2VOeZW5cSb5j3RYGZZIF9aPXccqYpyoS04pUqeqcdbfxn/ko/w//g+Wy+pE2kwRH3/lOdZYGB5G2h2rTaqNWo6pj3HtDd3kLWp72pYkrwTZ7ShmxhuR4KWjq18hOl1AEVLKsEYEhDGJbLSBruwlhf5mJhvox79suCdD1VrNsr1CWHra0QMBeQ7UQd+0OMeCLkK6tA6yiCBsTw5FM6E6HVX3Ju7/wZ1xw0z4KugMoNbwavR5lTSikEMy0lzhNS+3LVHgkDiFUz/c9j369QpCSkSggCtX59OMusZbkE0MtehO6FNPTZMTs6IubqDJBaFZIulZGRgLLItKqRJJYOFoJ26fNyk4UUgjVe6f+kE5QoBNXCALdqRFoUlqX/MXPvx1bE4mFdptCINExLhRjcA21GEaRjRAmllXFT2pYpno/XyfDSp1+2rccOk4RevozdV1qursktkZDugCicowZKzLSaSliZejOlaTToR+mZRyLJAhIdH5P4FRoLWv1oj88w0UrI0MqgWv6dHTruTU9DYaB7C1nykgnUGRkumRi9HT5TEjiuDtQRhKTentAtMs6Cl5GdaL+aHkkiluDwLNDtPX2uloZ0WSkbg3+zRiW6twynTZv/+NLsTwdfW8pMpL6RlpNpcylJRrDKGAYhzbXn8ioVE7jskv/lbPO/F+5KpLjGccJTUYC3bo7mxggJdP9AUlIU6XjoEizWEEaBl7g44UBphyjpMPLmtQYP1l1F61Eqzhl7CF+6vTPAWCQ0K6tAFDwBxkZ052BNyUN/5ruq0WsWatS1fJ517IZ99UP+grqR74vbZwEPNFjwTQJzZBIt5iOUaWpu0Eu+HaFk3Y6mTKyt2AgpEVi1gGljPSDZSab6ge4Zzv4tos0XR5wyggEk+lttwAhBS1HnXNoJXTTDhedXFs0DKq2WiDDxMdrL7JiFrP01Vh/1ZzOSpaAGpoWb9zzDQpaHVqWZR7WfpJEGASOQ+CW8fXNrhMGRJrI9KImUpORN+yC/3ZXj9UdtcB1dNt0Zb6nsz7U4hpbBfy2S0koQhOYNpEOTTPMgRdlX0Fdz5Lfw0hsQGKnykhQoBuXCPSim4Tqi/KDC07m/s1b8XQpYKHdoRAkRNocWYgltlZh4tjGtscQwqAT1rB0UqqvyWqSqkm2Q8upUtA5KYbnMqZzN7AHZERoT2lSiLB0SFq/q8lIpAlbr0eQkR2HeFGZcKVhEllF2ks6lr4XZcpIWqYxzTLpT0XB7NPx1TUTloU1NaUMrNoz0ulrA3XBQPYEJFrhiFojykhdf1aWADtWxxr1h8mInmMTtgaBZ4do6+31dqrjKmoyMkTgsyyRcJlizclMsKYOxqtmHTXKxBqG2ryaZ4wcFq47g2m6x/owcrwAcUKTkaSofvCmY8FY2KKQDBYkoWPGw6CUlRfGOk0EYDBGubMCQEPWKUw8hjf+KEWvyVXn/DWmMZCguxX1I10YysiY8QdkJNTlkdV6QWlUa1Qb6vmWbfFjc+pHNtJkxMekFIcURJ8FPYE11mSkaOoMDympdPoUuwklv4eQksAUbOy9EoSFkJJVPUk3XmZKZ0/0bReh7wbv9NRwvjT8DKAui4Sm9gVYCX2d5Cn0NZuWg0jxdrhCbBZYMc2sTJMqQIXuPjxNYELTotZrYevW4GUqPCLVgum7jjKAJmVCrYyYUUQQ6w6aqIXUvoIzOzY/ti+inqjtdk2ejO84VFY6vOjmmyEadID4vQIF3RESWJZ25JCZNmGQMVIM+ojYxjBiTD1UrxsU6CUDMiJ1mmhnSs/O0R0bjU6PQiBJE1iKEdiGJiOhlSWANoJyZo4NdPt7rAlc33ZIhJG19ZpFl1/c81VeFu5kVWmQ62PtEaS2E0/Powl1GqsiUyB7vWz/2C5R2p1VqoEwaGkyEvgHG1hVCmtZ79+nMzyFWQefJbY2Xfd1OmwSIbsxpi7bRFFryDMyKNNM2jaBjoIP2hUiPSwxSQbD8rIheQcoI3HcxQ+UKpmVaYbGFHg6pDCKGjo8TZdgtOeoVDoZw3CIoia93o4sYC0v0eTI8dzjhCYjxcQlkRJHCra1FkafdNL5J2XSKbVpW66ZFLJY9+VQ+U4mz/h71r/4LyjaPXa0tnAjLwWgW1QLkBcOuhBmQl0OkpJAR4hv0FJ8s1aluqK9C7ZgzJ/GSAwcnfrpS5upqEkMLGrTqZAxIKmZqkxkJSGGlIg4wpQyC/3aU1M/5hO+xJawLBrUl9QPcGIYWEItHPuNbUjkQBlBEZNIS/E9S+Bb6ngMrXJMysGgsU7UIHArLJtJVqaptpoImVBdeQhP36GHpkXPL2Dr1uBlWeFRTbB6abx4WMrICIlk0d+NlJL5/k7Q51/XM0hKutzWKFS58cUvJjYM1j6xmzMfeQQR6xknvTIF1HkEpo1+a0xnUO5aEmpBLPl9ktjOMkbixMAPHPy4MCAjOiDOKepBi3pRbnb7ioxkZRqJaeoBcLGRDWZb7JawdHppoFWUMD1WfY3LOvDMKhe5YvF7fHH/56gXBmTEXBGYuhnFSSPedV6Gqa+NDEOilBG6ReJF9R0T1ToA7WXtGekdbGCFwQLtmEFWpgGwZmdJrBCZzcFRio3v+yS9CEOTkX5/JWuhFdJkvKv+fzh9tdcoZ8pI2pYcRcNlmlFlpNfbpY+tls2aqQ0pI0W3Tmrt7veVOV0IE8NQpSzDsCmX1YC3ZvOevK03R45jiBOajOwLLVb0D+Q2XT+P+vqS2OrOrZ1UQKr/T1tRjcRmoqeNgqgfrvLs/dilJRba0/z1rv+H3XrCb1+rL240uPNenWgyIgRRUS0WJ/XUHWyjVsvKNH1L4IsaXuzhJGphCqTFVNhg2TRIhEAisKTJhaGRLRimnjMjtCEyLdU8MlYHYG1PkavdXherE+PoMDFD+1ICeRICkZlYQYWdRaZu0bQiAksdd7rIT8hB2aAbNel5VRqGzJSR8++7h7/86PsZbz2Gp2fvRKZJr18i0r6LZVlmn2nRsgtZ2q0MC0SajEgJe3vb+fLOa3moc1emWBVRx1LUbtEAWJie4t8uvhSALbv24mivSdevUtRkJDZN+tpjkRQGraUrqDvqYtAnSawh82oBIkkvLhLqtlWh244L2uxcs/R03W6AN0xGIompS0FxZGTKyFy7hKHLhal2FkaatGjlJ1VGrKrOpvGbOO50drzmiiIkAK6rPiNDB8KlCy+QqTSiUCJaUGUaY1yRorRMExyitReGMkeMhFZ/NGskTtduKTB9tT/f90m6IYY21Pa1UgOKjFQ6MSKRTA6lr/qtKrFfV9dIl+PCcDkbU39gmabb2wEMVBGA2pBnpGY7GUlJiYtplkf8DsOlmkiXafIo+Bw5nnuc0GTkf8mAh7X8vrWjFoDeoo2UBtJVP90N6oMyTbeFSAQCk5rupug5BVb26Lp4v8L/veXXWHSr7EOpFB1dXrXSce1AVXceACo5VEq2aLOf73lYSYypF9auNYYf1TJlJJIW42GD/akqIkoYGLiJxYqeyZEGSqVkpNJXd/3b64pcbND1+l2lFlHfoKhTWoWp7mL3FixCI6Qmixj6OCaSSuYLCK2E0FTHk5KRKQaLYydq0CpW6Qso6PqB5/eZajTwJDi6G8OwYiQGjwRb1H5jteA9VltN1zURiQ2xmykjUn9do2SZRJe3pAQz9aNoZSSd7LJ7wxq+f7YyKdqaDPb88axMA9BJKshEIIvL2WNLmoyU/D5C1Afpq2EREUrC2M0MrJYui5Rs9fmOaYmi04tGPCPFGISZekYEji4h7Gl5mHoGUqD9Q75W0SS6FVqTEUd/fvQbuM4wGREYmow4XptECgzdoWMZg3C9QJfWrHKFaL8ixPaUIpFpmSb0exj6fIe7aGx70EPc7Q9UJGvVLInezIgKlBPd9eX7JN0IQ2eY+P0VdXyOg2kaGAlUegmTjpWVaaJ+jfEZReLTGPhe73GkjBHCxB0iYOo57RcprB9cCyEyQlKzzOy4023TclOKakWRkVbznqEyTYUcOXI8tzihychM0Ga3LsGs0Z0YYdsiMB2kjoKfNydJ2wzHui1Mfatb6LcRSYIUgu8/OM7y3tPY9a/vhfY03ZLFPpSM3tHlBivS5rkwRGejZRj3JQVpYetuh+V6jZo2dXbtMZy+jZsoshJLi8lwmQVNRkypO0ewWU7JiF6MRRwhpcw6akJdAlnblYSJz65qj7hvUgzU4ty0TZxYEhmCXbWdCARbWgXOjNYzK+tIHV41Qka0H0MIkcnwnajBSkH9oKdlGq/fp9zp4mDhhHpGiaMWwPvkyeoa6bLQ9uoq2q5BsbMWgSDWZCQ1+wJIXcIQAmL9GdraEtHVAW4WCQ+ceioPrl3N2PKD6rFwPQYJji4vdZIKxB5WSSsFYYHllIwEfZJkMCSvExUgSghiNyvTpMFnFaOJKSMmdOdRr5+obhp9vKVIYmiFJYkTbFspCHuaIlPhAsMgDEOi1GgqdfCaJiP2eF3trN/Edaeya6GUEfX/VqFBP3GxdEeOaQwFmukpgFa1RvCYmnRbOGkjoMo0MpHEcTohW4wsysOmzp4/INb27KpsWJ8ZlqhIXU7MlBFNRnxF0lzXpTyu1JJ6J2HKtvH9ARnZdPpWdayhOqFEe5JcdxYhBqoHDHXSFDeMPF4/FBnpp8pIaWTbSlV31LR/SBCqzy4v0+TI8dzjhCYjqxOfJ/RCNqUzxxuRTacUZ3NpHnE2gDAw45Byv4uTtScGFHT5Y4dR54Hv/wT+ygbqiSDyzEwZaWm1oajr7FKIzLSaYlVfKwX6jnhhos6YJiY9p8rWlYVMGYkTi1rczMiIpUmKiZORkVj/aAsgSUZnwIDqpOlETeYmY2QiKOgyzR43YI0u4eytqgVna7yai6OtgETo8KrQkiRCGwXjgcog9bXsRA2WXfWjnyojTt/HiSKqvosVabKgF/nHWUtDVvAipYw8Xp2lVShR6CpZXjeEZGULADl0DX39HpZWRtpCoMyKBgjBjWduw27dBjLBC5Rvxk3SwXdlrMijVFSlscLKyVncejHoEcfxqDISSaLYyciI6zVJQgNDSKaZZ9pUpKbng+cPyjReAkLvR8ok84zsawZZcm9oWXTb7fRiYsjRMo05pn05/UY28h7AWBmUaezCMl3pYmgPhzk0diDUykihXMZ/VKXelk8/BSEgiSXdVkCUqM/dNCsIMfh5GF6gh8lI4bxzkTPqWhhRkRrDZGTgGQkCRUYcx6E6ocjITE/ykrFCNpfGsSbZcJpWyTQZSeG6h5jWe0DGSIpXTlSZsC3OrhRxdDls4C8ZVUZKxc2YZok47tJoqLybvEyTI8dzjxOWjMgowSrt4M6Jr7NkNiiH6kdqv+HRF1YWBf+4o34EK91FDMD1O0giZNKhqA2ti7Uavg4fqycGWAZtyvSSYuYpcXU7pDSgdcDd2RqdsBnqCO/l+hhjmpj4VpHpboKt75ITaVCPV7IyjaPHsUvcjIxEtkOkjZUyjjPPSPZ+3YRu1GC5HNDxClmnT8MKeKykvhKP6rHyNUfJ+InVQwdm0nNipF6ozDgYtMdqEtSJGjRx1VFpZSTUhGG862EFuqxlWtRL84DgbrmNCX0uj1dm6VUvQUiTyGoNlBFzQEaSITLS07HwKRlpmRGuN1gw+7bLfascFu1GZpD0tEGyI0sYsUfJU4tlYenUgTLi90mSZISMECZE0YCMmGbETluVCdbwBKtUbBt+IHSZZnDdpZmGjkU49jhxIllo+7S1/ya0bTrpZN3ERlcKM2XEqGtfTtDCTjunANEfGFitwgoN6WXKSLr4SgZkpOy5GRnxTt5Kqa7ev7GvhzB1W+8BHSXDC7QfDso09vQ0tavepq5FWGRMf0+Dvo/044yMhNpQ67ou1Un12AfGJ7i0HAEJUgq2nHcyrjeNotGDjjQ4XPpqWqYZJSN/fPJa7rnsdKZd++AyjTlKRoQwqVRU2nCaLmrn3TQ5cjznOGHJiLAMbl/zHeanr+ehwuM40RhSwh6nzr3Nc7Io+EWrDkClq++cex1a1jeJoscoakNro1yjZ6vta4lA2moWSSOcpoX6YXO1O1GQjAzJA1jVS0iAZUvdVTeqY9QDbbJ0HWqx7ohAkjgNanGD/VqKtnWqVoLLij0w5rVL+gc1jrIyTYq1WhmJDJ+9k9O4WoWJhJn5Y+4rqjvvopbqE6uHq3e/v1rJtrPjiF5mvYQg8QkTn07o4BBj6LbjtjZp1nolLB3E1RMe62pqUfyhOJmJWH0dd1ZnMe0LAeiUH0fq8lLawQOjykhfhCRSYumv87IVUxjygKTpsru85QEZ0dkjPUoInYWRRC5+dwuRUNsXgz5SyqzbpRMVELEkCi2SxCLQc1gWUNdqPTuZFeou3w9NPF99rrGUSBGBmVpIA2x7nMWOTyKhjTbRmiaNBfV6Ezvz1gyUkYFJWPYHAX0AVqC+U1Zhhd1JZaCMpAPnLAupy1wTQQfZ64Ft46xfT3lMvf/Snjamnc7dGVUHhhfoYTICEGkVw4iKTBhpK7EeipeSEe0BcRyH6qR6v9Zin5UF1eUS9ytsu3QdhjFoexZiqE33ADKSJH7W8nsgGQEw0u+n9ub0+7vV8RygjMDANyK1UT2fS5Mjx3OPE5aMAGzyNgPwqPcEUCShyr3lTezzV2E6arHqon7ky7oboNjtkpgdpEgoaWWkWa7R1gbGohS4Ela1EprBVKaMxLoMVJStg5SR2Z6kZcHda1VmQrNWpxLqzhXbxNaLaWAEGM5+yjSyMo2tFyyEMzCwAo2KuiM0o4Bi4GPrDhYvllRDaMoVBD67J6dxslbbQU3+weKgVRcUGTH1KKP29GBhsOOInggItJG1qzsSOkmBQpoxkpjsq+koeL+MqYPcekaB9eXtCBL2iUmqoo+Qko7t0RUuodUicJeQumMlcAedIXLIeNMSPunctQTJsiEoFhU5lKjwMID9VsgcFnEwyBrpyiKRHvTn9SdpJMokWZENTKmDuTJlRC2ssZ650tUEoK3bSTaykzGRLtQCU3tj+kCizasAwujjOOPsa+p2WlHLTMfLKRkRTpbHMlBGxsFS18D//l9n+6v+wqupv1FNwbYKyzyRlDE18Yl0u3bgaI9NkjC2X4WIuRs3IiyLyrj6PJb2dAZR8AeQkeEFOoxGyW0YrahjDkvUEhMkJD09Z0eoa5TOfBlWRloLfbbfoybqCjlOfaaot1HkzhjyuxzYSdPr7QYSTLOI4xycRpsiLdNInV9zoDICUNEdNSny0LMcOZ57nLBkJEkipiz1Q/hAQcWRh/Eq7h87jUpJ1/Zjk1gvwHUd4V5qt5GGgZBGpox0C1W6VkAnTULtJWxaiVkO1hIKtQiEvrrUFVo0U2VEL0Cr+glNO2ax2CAWMZYBpvZxhLaFYanXBmYA7j5KYkBGRJKqBcMGVmiPqR9hK+wjgIm+DifrJwhghRUEkn1j4xS0MtK3BsmKc4VRX0tsdRH6Dr5d0C3EcYSBUiYcncrYiRokhoOfOHi6mdRMHHbrBagQVjBDvZgbBcqyx2aUhB55+xnTxGDRTGiXHwNBRkb8YTIypIzcPibZodWZnlCR+d5QmSbUU31Dw+IONyLq1yhoZaQrilm+h92bzDJGxljC1oQ0jWvv6bt8AnU8XV99jkGiHl/DE7hJREF3HclAkZk+kkSXaERiYTpdHGeC/W31GUtjDFt3Pi0vaxOl6eAl6rFMGalUwFPHN0xGCq/7MawLfgoAw4ywnQCp5/YspSVAXaIxopjKnPJPuFuVPyNTRvZ2MHRb78FlmsHfowPISKRLMCIqYCIo4CC0Dyrt5kl0+qnyjKjr1VjosftR9W+vWJ7J9pf6YYYj2Q+Mgs8G5BU2PGk0+XAXEBxOGTlr9FzzMk2OHM85TlgyYhgWs9rnsaOkfhD7wTo8dxtlTy0+e8MNuvU2YUaHopV6PaxEYkiZeUYiq0pfJDQM9cNf70hOWolY8tUPqJP06ev0yxJd2loZEfEKQiZsaSU0zT5SSFacFWAwNM23zSwZ1Td8EncfJdHMPCMyTg2s5ggZmavX1eN6P/WeWgzX6eTLZaHep1csqQnEQM8djGcPDUEvHiw6idUj0XfZXUctiJZOyUw9G6ADz5wKSWJkGSMisZmb0K+RVUwdod41Cthhwpk8oM7Pm2dCWwWecJZpFfcSIyANQisMjm/YM3LjRMInbKXIdIVEIjBsdWwCiCwHCQRYPGDHBP0qRfSMG+EhdHu33Ztkv54DMM4Sns4OMXSZpqdJRyr4pMqI1GWeSfZixJKyLnXISJ1MF5lNrjUiD6vQwrJq7NfKiGGNY2kVZKWhu05sDy9Rz2fKSKUCuuziW4PgMT/YR78FUV8ttGPuCompzml/SweqaWWEKKGwRy3kzhZNRnR3y+KeziEzRtTfB2pBFI96kCLdtp7E2pMiPYSWqtLFP07UtVTKiHq/biMgitS/q/rk2mx/aduyIQafsXtQ4NnoTJrD4UAycihlpFBYP6L85GQkR47nHicsGQGwe2pM/KLo0zFbPBz8LOftnSDRXRv3SPV8rbePUqQWHLfv44QRhowzZSQxa/hGxIpe1MY6MZtaMa1A3e1VaLIwfjdx0KBEm5alFtVC++u89v4vMuNL2to42NT7iHVrb882SXRwVWAGRN4+SrKZRcHLWHsFMEc8IyvVErHwEHqRm+iq/Z/cUovEgqnuwPuel3XTdIfICELQkoNFJ7F6xFpyD/Q01IyMMEpGem4VEFmZxkgclis1YiGIrCpGSkZMlb56Ko9iyITY7rNGqEV7e3EfgRnQN4a8IUNkZFgZKYQBRpyqLer8euFgW1sIEAYhFrGAZVmnqOfTdJIyGOo8Or1xFrQxps4SXkFtE+uFPdTKkKlLY31tenZkRB8XixBpQFmT3LSC1oWMjIjYxSslCGGwr6Uec5xJbN1qvKy7aRzHoyA6iCShqEPGlDKiFsq+M/inG/j76bUCop76XMa8leyc9i60R8yrMgZr5w4A3JMUGUnLNP12iGmPzqVJMbxAJweSEa2UhFJPOpYuhi5R2annaIiMeGUbS4faWalxuDDIEMkC3YYUj4MDz9JOmvU8GVLPyOA8DiYjQojMNwJ5mSZHjmOBE5qM7HYupGwoX8EP13ydyFgDgNS5D/eYasT4RGcnrlYgPL+PE4VIGWfKSGJUCYyYFb0QjrUT1nQkvUD5Lupihe7YzXz1lG9yy9gCTf2DaMYN1ujhYa00nyLS3Svax9FyLBKp6/5GQOjuQ4o2vjYjJpEmCKZNbAx+vJvlKqFZROj9XLDrMX71YZ837wyJZcySDl5LLBdPd9N0vRIiHnQxLGsTJKgyjW/qqG5NRky9gPYPUEbajjq/gl4QjcTGNKosVWwCp5KREd9wIBR4BNQ0oZm21J3ysuESGiE9nWdCInETk1g4qpNnyN/iBT627v/t6oe7wWDRqSd9pGEQ6q/7bmuMmqPMwn3DI9Kk6XP9Kt8pqGMeYxnXVXf8IlUhdMnB0tfIj/T0V7HCHp24G1oGZe276OnvQweZeUZE5FGuqfPf11KPlbwpbF2SafQU8Sh4BaqFPRT9PoYuIxnDZZqJweLsBykZqatjdxvZ8L1uL2GlXs+UkSQBUjKydVQZAbIyzYFZGyMLtDxQGdHnm6RkxBu0bzs6q0SXxRzHUYu/bu+1dPLtcKKsm7Yt6/M2zfJBQWRHqoyknpEUB+aMpBj2jeQG1hw5nnuc0GTkln1lqom647t/6iZKRkzPCkmshASDB231Y/1LO1dlcexu38fxQyRxZmBNzCq+EWVlmrF2zJQv6UfqB7RGg6omOHdUerR17LpIelS0stESqjuigSIwZqQW56ZtYvnnA+AbAdIMuF+HV9mRQahDzzqOpR/Td/nFGn2ziKGVETto85+2B4wHkl7UxNd31sKwM2UkMa0sOwVgvzPoS03sHj098Tcx0zJN6tMYkJFu1GRZe2KKYkBGnKTCfL2MNCxENPCmJPr/bT1TpGTPA5IwmsA3hpSRWFLxITTdgSqiW3kLoY+rlauOfqoXDJSRauSDYWZ37vuNMUoiTZO1EbZaKB/sjfGQPrQxlvA8tch6Ouwtvcu3dNx+oD/fMVay+P/IdahoZaQnJI5tEBiQ6Ch9Yo9qXX3nUgNrpTiNJfXwQF3OKxYLVCs7Mr8ItoPhunDe22H9JfirT87OL/D30W2GhFoZmSg08KzBhOD52dlMGRFxAmknzbp16v3HBmTEPMRcGhhdoIUc7aYJdfBdX/urStLF1S2+lqPOVWrDsOuqC5yaWL2qNrY6w8qIIiOp6dTzVh3kC+keJvDsQBxUpjmEMgKDWHjDcDBN75Db5MiR49nDCUtGWn5IS8yyq3YNifDYKRpYk/fSrs6ROBGPs5GuUaQQSV60qH+cZIITBLhBCIKhMk2VvghZ0abBiXaEK9TsFYAqDSb0c3MOLGdeii5VTUbaxgoSQZtZImlQ0GSk4ZhI1MLq6xCs2wp6FotvEOi47Y7uGClqpcV36nQtN4uEH1UvmviOOjYvjrCSBFurI64/ICNzxeFcjx4dSxGlxFCLsJ2k7bGjysiynjBc1NkiRuzgxXX2jakFTYZJpgTEoTruRrGGEUVIK2ZGNJGJSxuDvjkgI+V+gm9amV8knUhbCAIK2vja0aUqPxh0YriJRBoGgU7SXTDGsmF5kQOOnuey2BtHaqI3xhKu9owU7LScUtfnrTtVpM5iMZZ5ArWwLxUrA2VEgOuZRKYg0eRARh4VfR3SMs14ZRZr6BoCeMWEanE3ZV1eM8p6ET39DfBL1+MPEQI/2E+3OVBGpopN3Cx63mJu9aosfdXU5T9340aEJihuycLS5PRwnpFhZcISrZHnEt1JFWgTc4UCro6ytz31OiF8fQ3V5732FEUSvJouSw0lyqbEJNGeGe+AwLMkibJW3QMDzw6EaZYQYmCEPZSBFaBeuwDTLFMun/6k+8uRI8ezgxOWjDhS0N1UY6m2Fr/0YvaEBs1N36FUeRTphNzD2QBcuBQR6oVCxDGCgSm01NELgnAILCPzjFS6EguJr8O6qjSYtAYqQzvWE0Rlj6qW+ltmh9haRew6dJJSVjrpWIJYLyx9qUjRrZ76b6kXEyaKqLTs0bTOxPRYLlRYqOox6iIh1N0t3aiJr5UUU9+Rp5Hw9aCVyeO7KoM7xMTq0jUUGZG6XOGm8rxI7+oDgqRPQ5MnJyUjiU3Zr7N3Qi1MTtCmpH0HsR6ktqs6zuQ+1bF0qlSD0ZqyiD9Upin1Jb6TZMqIzMiITzHRHS6u7jwaUl9EYiAtO1NG9pnj2bA8UVTXOYotemEBqb0MYyxRKHQQIsbRJY+yp0potm75JU1qNQfKyIJboOao7pJlM8H1LCJLZN00SeziFdTCm3bTTNUmscXAkArgFHcyxhIlXbYxq6PkwPfns/8Pgn0jZZrxwgqWodSbOLZZmJig52klQn+vnC0nDa6PEFR0qWbQTTOqjAhhIIS6po4Y7aZB6u4z/R5lUcBLlZFCTb++D8hMGTnrFWv5xf96GVKocpk7lCibdtPEmuwWi5sPOPe9SBliGA6uO8uTQYjBHCA4tIFVvecEl136Xc479/NPur8cOXI8OzhhyYjrWVziqWmhvcqr2B2atCa/z6mrbgG3z72odr+LF+NssZXpCHbdJVEMfCz94x7YRdoCIgGGFEQI+nZKRppMWEn23kGoundEFGbKSMvsEDnriB1oRw5uFCI0KegWVV29r4nLD91UBYkJUmVFKx2lXhvHVyRpoTbBPaeeR2ikIWPqPDpRA1+TF/QiWNavKQUdZhfVIvBYffDDHVt9OmISKVww1IJS0EFWqerS0YPG2pqspGREJA6loMa+cV2+6Tco6gTUro7Lf7xaZ/0u1eI7Y+n8lriMr5URkShlpO/2MzISp6WzOKSoPxqpVY5Ik5HAtABBXCwTamVkyRzLummW7To+Dn6/xFZrATSZUZ6RDpaePSOlYEwHwVlCB3pZipwUjA7zehZRyWhw2oSag7PDShAFk9gyMgNrEjnYzgRSyqxMM1MtYJuD7weA6T7IZh7lJ5+4Wf29OlAmksQnDJeyv0dRi267PTCwuivZc8XiGLFpsne1UheKgXpPV3fSpEh9I4MyzcEdJWnuh2N0szlEUiakaalWRR1jKXFxdcnGLqZkRKXVpsqIMARO0c/UD2eoTOMOlWm2bPkAGze+a+Q4UvOq560fiaw/HIZLNYdTRtR2dUzTPezzOXLkePZwwpIRgC31hzBkTGyvou2ezWIkmKo/ROiGPMhpAJy7FLNo6NHieu5GqoxYcYLjq8UvsAsgYEX/lvVFkN2lV2kwYUlMnTAqgodAJrwh6FHRU06bZofIXg+uQSt0EDDI/3B09Hg6n0bXz50IfFkBCU1NRgq9Nq6vzLCLtTHu23oOPZ2z0dcKTzdqEmmPBkItglXtf6l3+lx+p0q23D42tABaXVrGJLE5+GEv6iCrPgEJko4eNNbWd5+FIc9IKajT8zRJCZoUYh3HrslI3A9Yu3sXSEnRSigQ0ImrBOk8mkRih126xXZGRnw9l0cgKWjV53RXLd6RTkdtu2r/UaGSkZG2WeckHmFMLrIixvkCb8Pvl9lSWMpauSuyiWFEFAuKYCWiRL1Q0N8Ddc0KxXES3cUTYhPhYhOxvrKbcW+ZSMCjZoS0jSwKPo5cHHucZj/C1+02UxUX2x79pxiLOxHA5sc18akMFlHfVyTaMJysBBEG+zNlpGyvACBx2LxZkY5uSV37mv6c006aFOUxdS2NNPTsEO2tlvYCeXafXhjr69zOml5KE0o5KyQ2nh6YZxXLWZKqaYaZMgJK0VH7rYz4NEyziKm/Q5MTLz8o1CyNdi/+CL9IimEycjhlJEeOHMcWT4mMfOITn2Djxo14nsfFF1/MrbfeekSvu+666xBC8IY3vOGpvO0zjm5/iSl9h9mrXMGe0EB0SjxaWEUkbKb9kB2lhO2uWpyLOmfDCgdkxNR3mpGtfmRXPD0fJTLoeKNlmg0L42p8W9LCCnfyul6bcqzLLGaH2FmPadtEOgXUiXTLrU7dPD/eM3L8XryewKljIljRNX+v18IKlfS9+6QN9L1itqD3hhSMRPtZ0rj2qh5+Vu5L1s3N4/h99nsGiVaDEqtHU0wQW0O1fU0opFDD6jpRg0hY+IaDSYKjFQQjsSlEZYqRWhScoJWRkVQZ2fLwY7hBQEXnbKwymvSiGkFmVgUjbrBreplEex260smi3NOANa+4os5Vt912i3qkvVdB6mnGNaeEm8T8Cv8TgG+K13A755N46joV/T5Rol5fKqvvhzAqVAs2AomtCdxYpUqs4+VrNLELqpwgBJw79QMA7okCcIaVERvbGWe/9otUPQvPNnGd0X+KwthDElsky2r/RmUojl2XaFxnFtdV7eMJi5mB1dNt4lIU2bx5tMRRa6vrk3bSpKiMeyAiTK0EHaqjJDXweqZPWw9+DHXiLsDUqikwBAJBLZ0mXbIzYmGaYaaMgPK6wKgqkiJVR1LCMoxedweg8kGOBCNk5EmUkRw5chw7HDUZ+eIXv8jVV1/Nhz/8Ye68807OPvtsrrjiCvbtO/hHYxg7duzgN37jN3jJS17ylA/2mYa1MMfmzi6EjAkLZ/JA/zRkt8x91jYALlqMuGHGZK9uNx2X+kdVd5/YcYypiUmiCcNyQV3SRmTQGVFGEjYvrgVt7HN69+CHxqCbRisjBaOCSAr0pYWbttxqovPm+I6R46/FL6ZqlXCBZV0SKvRaWPEKAP0J9SNsa1NqSka6UUPNe5GQ9ijU9PyarieQyRLjjQViQ7BLCyihsYgZSiJnsIgV4hCpO0v6IqATNelYZRACV5doYgSRfpOpjjJ5OkGTYqqMGAW6pss5D9yn9tlU6spqs4kfjY8oI6ZYYfd0j111dV5NbIT2jVi6jfiHxZcBoBubMGs1ffxpLolkc9EiCWucyQ+4Qv4TAH839kYWS2rbYuAjHFV2KZfV8VhWjYpnYTIop2x3ijRlHYA6y5RKg2tz/szdAHy/2wPbzDwjcWTj2IMo+KmK+mxdd7BI27bAMBJ6C1sx9XfAGFFG5vRrZrJF2/IaEJUwjIHyEEvvIDJS7A9m0gyjPOZlIxDU+R5CGbG158cM6OrBj7uXBv/u10xMYFa1eqh/WhbayxkBsKxgVBnxFRkZ9oukSAlKqgINo3uEbb2DfQ08I4dr7c2RI8exxVGTkY9//OO8853v5Morr2Tbtm188pOfpFgs8ulPf/qwr4njmLe85S38/u///kE/jscMccTP33Mnm/u7OXPlJgDutH+CJCxwj/aLTPUECy6s6DvNSVkBy8XVaogVJxixWgSlre7wl8vqki7Fkq47UEYqJkwYJWa0z8Hp3cPOZDqTsztCkphjhLaDI2ExKWbTdPv6bvIMsZvJaJADUg4meN0+gSuNbC5NodfB1GQkRaWp1Z+sTNPCQGAnNoamI1VfqyaehYyXGWsodeU3ZiXXTt5KFK9Q7vfwixdl+3XjGKFbUXsioBOt0NQ/9mmJpm/YtD11vSY7yuTpBC2KevZOxyzQMTzOefh+AJK+IjGrjSZJ4tJJa/gShKHOI71mS1hZR01ihMRIqjOq5dXRSkltWi10qXnVJmZrpUSoVZpf4HOslrvomSW+d9IZ+hgSil5KRvR7OjWqno2tyYiU8Ol2xPayzpJhmVp50G67dWw7RSnpxAmtJBkYWEMTx5lgx6IiiDNVRWK9wqD7x7a1t2f+NKwoDTw7hDLizmSLtlloUKg4mVICECYelUqFsWRAoOwgxN24IeukSVEZdzG0X0RQxDAsDoSlh0Y6RsinbtzOz/+vm/jtv/tm9rznVDBro56Lz/1/XyCKdCfPAWUaX6sewxkjKVKCEgT7D3qulwWeHWmZRpERw3AxDPtHbJ0jR45jgaMiI0EQcMcdd3D55ZcPdmAYXH755dx0002Hfd0f/MEfMD09zTve8Y4jeh/f92k2myN/nnGYFndPX8Ka/j7+w94vA7C7fC5PJKezU2wC4IGqSSHspc0lFHExx7ek8RaYEmxdO5eWnrdRFAjgjLqF1CFkho7LrlUbXOTrWnrwMNu7ypcSE2NG4yr11IPC8gMsyFKmjPRtF1NGqCbfgXzuhh5rO+ANkRHH97GHpqqu3f0YXi/tegnoRS0SYiwEXqwWwkjEVPQclaZbJmKZuiYjO+sW91sdZM/EjQJiZ23WbeOGUWay3dF7mL297bStEpYki4LvYdPydNaJ9ng4QZOSnkrcMQuEkU2p36NVrOBTgkRSFCF10adtDLwEXdRMlYL+PPaLUTLSscAqq+1tPYF17fQMjiUyv4glEjZVa/QTdR0dAn6NPwOZEOqSUKtvM6YHvRW1ebjojVHxrMy8GmJw0UQF2VX7+bHWEhMVTUakKn9t1arRfD/MyjRxbGLb43z1LtWaeulJisx4pYE/xzDU9yVYPjMjI6PKyICMpIu25TUoVEe7SxY6JnEiWTdEPJwwyGLgh1Ee97K2XtM8dBx6Wu6wjYjP3byTWx9b4sLZ27LnLauEWR/KkEHSDfssLqrvn2mNlmkCrXocUhnRBMU/oEwjZUKvp78HR0xGdEhf7hfJkeN5i6MiIwsLC8RxzMzMzMjjMzMzzM3NHfI1N954I3/913/Ntddee8Tv85GPfIRarZb9WafDmZ5pVDa+jrX+PG9YvBMreAxpOPzZxp8GYEO0h1smLey4naWvFqSDmDqFUKefCiEoBrrTxiwDCT07YdYWyKI2lIYxjVitnlOFRS4JXGJzDEHEXEnddbfMLtORWkRaxZhq+wkWkhKezhrpWw5VFMFYdAZ33/u5g6KxTEXGg7k0iaQcDOT2kx69YyRrpBM1iQ2TQpRk54U0KPra6+KWIV7JlJGkaNGPXJKeTZB2tkidu+GHoNtcH+3dRyJj2mYZTw4pI9KmlbKHFEmPYjBkYO2p97775NMxzDqirxbx1UYjS2AVUtIwdlBIBJ4mBMtDZZrECOlagq83m5TtFmef+h3Gxp9g6/g4E2MegdSJoCJhU7FIJxn4CDbxGF5veXB4/YSJfbrjSXtqHKdGZUgZiTD5q3NP4tQ9ikSc0lukVNqq96BecwpKEXq81c8SWJPIYOeKxa07ljAEvPF89d0uVurZ+1tWD9seQ0SbB8pIeUBW+lmZZjbL57AKDYrVUWWk6dvc+MgC64qD0oQdhAeZV0EZWLMo+MOQEcfRi7oR8cpTp/gvr1/LRavuzZ43zeKIMmKVHM4979xMGXHsGMsaKC6ZZ8Q9mIy42rSalnIGr9lHkvQRwsQ7YF7N4ZCmsOZ+kRw5nr94VrtpWq0Wb33rW7n22muZnDz8mO8Dcc0119BoNLI/u3btelaOb1/QZE1/nnoiqTS+AcCjJXWnus3fTiIE/aiRKQgF6SCmTyNK58IYJrV+ml5aAyPAsyWbXYMlTQ4mfMlCqI2TxRUwioSeClbquuquu2l2OKevouj7joNAsjiUNdK3HYqix33JBpacrWzzYlYZkj3WE6yf/nsczMwzYhgh9W4bkfQQYYOppdsQcTpDJtQZIy5uYmZkxIqKFP20+6SMkYSMNdTiLIsWfuwS+TbNQjptWJtFu4O4+LTDpW2VKCcCT3tG+rFFszCanjk3XqYQDMo0ZlPt7+bTzyRyJnA0OVllNJW3BTATaHkLXKyH0yVS4AsLqYPgpBESWjG+bfCGk77GzMxjnHzy99hSsZmeHh8oI4ZkY8HNvB4AfVzM0EEs6cF1nQ7O7odHjtmyqlQLFpZuY42kQdWzMXVibmSuUCisVZ4N3V6y2exQtE2WwjhTRmQk+NIdShV5+SnTzNbUd6tYm8jey7Z9xsYuwSu5A2VkqLV3VBlRCoLlHVym6ccuf3fbLtbV69hBgB0EKrTvEMqIZZtUptW5Oc6h49AdWx2jIeDat53NS9fdCXLwHTDNElZtoHwYRYvXv/71TE0qwlWujJZ+0hKMewgDa+YZOaBM0+umbb1rjrjkUiypTJUjNbzmyJHjucfBheEnweTkJKZpMj8/P/L4/Pw8s7MHhw89+uij7Nixg9e97nXZY4muX1uWxYMPPshJJ5100Otc1x2pLT9b2BOs8Ap95+W1b8OtvwXfVovd+v4uKAHd3mAujXRwq+uIbbWASMNkuq8nlpoVLKPBSzsmk5bkTv07ORWAFc9AaQ8Fr8tSUiLwzsDr3EhJT4FtmR3Oaam7vMQwCV2bLg5eoPNFbIciPb4Yv5wfZ46fmfJpR4Ive4JLjev5GqfTTwPWjB69EOpzH0TIAN8RiKYumYiAduQTWg6WtDKS5UQl3EAdcK9QQgLjLb3IeCZdWWC7sYW+4+rFRxEEtxWA1JNrTfVV6pglKokYlGkSi6gwmCMD8PiqSQr+QBkxGwFQ4I7TzmS6sZupxhP4RMwaLTDVeVmxpGs3Oa+3lseBHio/xMdGoJQRXy4wkSzy4rWqvddxfKzmV1m1ej3hDxS5cAxY6zmsUM+OZ4EpTi/U+P53dyGrDk5nidu8bbgMSLBtKWXEEqkyYuDZBpYOgoutZYQwKRW30Gr/EIBiZY5Xr51gx44VEq06yBj+jyYjP3/BQPErj68CVBieZfuMj78Yt2gNeUaGycjAwBrFSjGzvAZFb7RM40cuX39gjmtOK/LjX1dk20wS3C0H/5sDOOfHx9nxBLhe/ZDPD7fYRlGHPXv+Lvu7aRYRwhhRRoyijRCCVas2sXvPzZx33mkj+/P9tJvmEMqILtMc6BlJ23qPtEQDUCmfyoUXfCUnIzlyPI9xVMqI4zicf/753HDDDdljSZJwww03cMkllxy0/amnnso999zD3Xffnf15/etfzyte8QruvvvuZ638ciQIg5i9vQar/X08YtuYIuaNu5QSYcmQCX8FACOKMPRlisMuQhiUqxsBSCyL9X3VbpsYVbB8XqUab9ihF9F6KNnSuAAAu9CnZZUItDIy3EkzsQK2Lqc007vgQL2vbzu4MuCf5IW8pvbvAJQtyRu3PEAh8dXMEsBKEipJghd7WNE8ZrxM10myyb0t0eeR5p0kpoE5pIwYsY3QrZqxaRE4Lk5SwA3UY227ys21iwEwgyeQ2sfhNQZJnFLL74FdoTJUpukZNt3CwPfRs7rMT6+joBWljlkg6QnmxmeYn5hicWINjjgTmdjYIsHRYXFmEoOAU6SS3Hs64XNZ8+nECGkYTd4S/Sm2ERPqmPldu65l3YYNmYHVcSxsQxAxmF2zwBQvHq8gEjBWAgqix2c7ryWRA0XHsmtUXAtbKyOJEAghMr+D9JbZv/+bGTkAWHPJp3jtul/n3S/5fcKC+mLIWLDQ9pksO7zytIEiUB5fk5mBbctnfOzFarptnHpG1HdCSpl1mLjuqkxVcGtPENY/QKt1X7bPklchjCW37+tTabeptNskpomz4dALuekcemJviuEW2ZWVO+h0HkKI1JyqW3nrw2RE54uk3TRDgxdh0LbrHsLAmhKUA7tpBtN6Nx7yGA+HavUsbLt+VK/JkSPHc4ejLtNcffXVXHvttfzN3/wN999/P+9617vodDpceeWVALztbW/jmmuuAcDzPM4444yRP/V6nUqlwhlnnDFiZnuuYdkGt0yczIsv/H/585kL+NnFy7lyh8WpwS5eyz+woo2iTqRjy0XAkp6HUa0og2ts26wL1V2wNCuMWwkbutrQqTWnMV9yypK6I5SFmJZZRJp1EBMZGekYPt3OHJ7u0tk9pe5uQ1+rFZbDvKxx6aabKThNYj13pbSmT6dgsrustq/6ESQV3GhoCmt/PJvcGxHTjdsY0sEOyZQRI3Fo08PWLctdr0TgudTbOj7eLXP7hBrWZ4dPgNAlj1YnK0mk82JmqjUcaQzKNJZLY6hTpOv08cfWUmilykiR2DfZvk61Uy/UyhhGBT9W19/WZMSKA06qnQS6vNLVHTWLuvwijRC7sp+LC6q89MD9L6HbrxCGS4TRv2UNuSVTkYk9xbXIQHkjGkzziqmBT8LF59ZkG01/8JhtVTEMQUkfj9Tn7RV1WcRt8oN7fiXr9ACVKWKYZXqhhxEW8Ja30O2ohflnzluLbQ7++bm1Vdi6TbxQKFEorMEr2gcpI2G4hNTx6647Ram0hbh7EsKQROJ25ua+nO3zpBm1yH9714A0BrNrD+qkSRFGDX2uhyYjwyRlbv6r6vy1byNtmT1QGYFB0NgwUYvjPpFO7H0yZSSKGsR69g0MT+vNVY4cOV5IOGoy8qY3vYmPfexjfOhDH+Kcc87h7rvv5vrrr89MrTt37mTv3r3P+IE+0+hFPcLEY1dhFQ9ZZ/Jzi1dQD+H9rT/l5/kC86aS3239QxgLn319JRGPlRQZiWwXx75Pae/AjxtjGEA3abKoPSNRvILd022KnqClu27ceE1GRnpmxNjOeynoIXf7x9R7d/TdvW87POJM8JqNSpGau/3tyPnVSENwz8l1ejp9teqHJI6L2SsjYpckrOM1VynPiJQIocoplixhB/EQGbGJ5OOUdTpnt1BCFAV1PQ+tMzvBilejYgjK/UEMue2HeJ46RmnaxBicG96LLaGgyzSG49IoDZER18etrqXQGygjcWAwP6kI20JNT9YNFREw9YBBM+lzWf0y/nVJXZsVqVupxUAZecVpj4KMeLy5iZWV1Ty+W80X2rnrU5R0h0pVk5Fxz+HfnXP0Y7OsrhdBH3M6UC9hcMdu6tyNkqVbq/W/nGJtlrCjvyvdKUr7z8pes/+uX+AVL/s+n3v0r9j67b9kw22/S0ebmYdLNABmqZKRkXplI6AH2EWjykjqF7HtCQzDwTAcFu/8A7b/8x8wVfl1arULsn2etfE8XMvg4fbAQByv38jhEIWKHBwqYwRGU1n3778eGGqzLap/E0bJzkprqTKSDtmLogEZScsvhuEcOtPEqmXpssOlmt4RTuvNkSPH8YWnZGC96qqrePzxx/F9n1tuuYWLL744e+473/kOn/3sZw/72s9+9rN89atffSpv+4yiYBWo6CFkb5t7EZ50uLfwMNJRP5j7dOeArWdnxEaQkZFaYRWWsIkdh8dqEYYe+naxNjPew/Ys8Gy3sR2rPw6JiTQEgTaarhH1bC5NKAyKex/KyIjhqdfu8wdkZPNJ9+KYIb3FKVpPnEd0z4sQiaRTE/TL6se64gdqIFwkcR/8T3S2vwdM5akQcdqCbGMkZYxQUgnqAEQEeEuPUdb+l55Xoriyk3FtLI0mlAT/2uk6BZ14amRDzMp6vxZtq8RL+S4WCZ4u06yrVWgWLWL9944Tsc4qYeoktI5ZIApMemXVibK/qvb/eKJHz2v1wI0byDsl/dhgISnyQKyIwkKq0ngrTNa+B8DijlMA2Dl/Dq6xhjBc5Kx1KjDuwva34fbPsMmKmERdt63lzUyUnGxOT1v7YKbHNmXfl6997Ts88cQTFE0dca9tMMVqgce+/kGat/4PTt13LdOPvDV7jeO0YOctvHQ2INIdNj4xF2wYY8v0aGeHKBTw+kotmq2pjinPBVNPNzYPICPekDek2wwJWqtYveoXOe/cz5FG2a2ZeSk/ceaqLMEXwNh4+JyfUCsVhyvTHJzKajA19RNsO+1POOP0P1fnYYhMHRmQEXWucTSY9hsMpa8KITgQqgSmO2qCfUgpeWzHJzI/Tql4aN9Ljhw5jk+csLNpkiShGrY5dynipSvTSBK+U/kylqd+kPc6+m5Xj4uPdSdKO2ljCJMJbw2JZ/KgU8bQpZzItonp8z3n8SzwbN5c4JGyhd1TP6yxqxalLWuSjIyYsojpNCnocorpGVhGyEo4+HjWzuwikYJd95wJCML2DGPzaqFanlHlo7IfskSB2Ajox3VIihQd9R5CtwlL0+JR0yaMLQqRzkax5kBCtT9QRuJem4mVUfPg3vuXKOj5OnbSw59czWJLLzCGSceqcHFlJ66dZGWal2xYRd21abtq3x0v4ZSNY5ioBbJjFlgs1jA95alYKZsEFuwybHqmS6Rj609v7aPb7FKwBd8MTs6mDVeE2u+69fcgRERjZZrOyiRLSYEfeFu4Z+9rAdi8/kEcI2AsWYZ/fB8b7vsiUyg/wrk/+Bzev/8JQvtQknCMC9ZVWTszaKPes3uFT33qU6yJ9B29JkmFsk0SlvDDGaZ/6Uw2/MZPkgbRuMU98OlXccXd76KbDj0U8PMXHuyVEkJw/n0/5Nw77uS0dS9Vr9chdQBGWS3ow+ZVgCRO6Hf05OWqg2HYOPq7GwTz/PwF6+haQ+22hzCMp4h+VJnmAAVj06b3cNaZf8GqVT+Tza0BhsiI9pNYB5dp/CdJX02RZo30+3u4//7fZvv2jwOwYf2vUCxuPOzrcuTIcfzhqLppXkiQMmA8WODXHpkFbBLuZHrhAgxLJYHut9Nx8T7gIhN1qeaiebY4Zaa99SyZD7H5/tO4aa1SCZYcwV3OIyx6i3S0umElgr/d4HBlb4qwNI+lkzgrhs94pBaUYlLg+ldN0ksUqUncGNPoE0U2xTCia1u0qPCDvVuxuyr2LJQFvvnEyZw6tURbzzUpBhF7Oi6TSz69Sd0yapiAyNp7pe0wZduUxVYS1Fj3B2ce5Kb1e6nryb29YpHNLLC+92X+kf8MwJjfwLv935g+ucJDQDXq06lezET1bpqNMTAMkpLHHRe8ikvmrmW8/jhh6NLYdz+v33M6bXcztX6djivYYe6n7akFqmMWeHSDg7/5s/xmNMlq83HkZev47Z2ns71XItRdOtNJizVrdrJ21V5+j39if2eSOxfOYlP9RRg7esyuUt0y8/suZt/qc/nn7RZhocw9M6/kY3yVaXsfL193Iw8szNCUBhfuvZW7ThIgYGr3bfD4Tdjyb/Tyb/JLL91CwRtk55x66nncffcjOJoEWWn8fkWpVz3dfWS6DgYVEprYpRUY28RaafJ/H7iPqjNDp7DCa89cdcjv5FQYM/bww6AVEsfwCYDI9Pj0bynVp771Nuonw2N3Jtz+v/9NTc+VyrrjldXi77ozBMECvj/PizZvY2JmYDwtnHxwW2+KQZmmcsjnDcPCNEvEcQcwWL365w65XfmSVbQB71Sd75F6RobKNE+WvpoiJSoPPPhB7S8xOOXk32Pt2rcc9jU5cuQ4PnHCkpEkMXnJ/W122A+z3Up4oreMXVB3hnFk8vp/+woArnDAhFJDGRf3d7azxTmJjeXT2T92JiXH4YyuwZ1l+Lv1NvtXCSIuoFnU2SLtgOtPsXjHHnW3+grxfS5Y+nPsJKKw5Z+YS6Bi9qh1qry19GXmZQ17OkKKABnDV41fossEfyt/ESFOwjtb4kYC2AZs446VRR7SxKM88QAXnqZ8JbO28nZsC0w4ZT9xoUniuLAV1okhT4802Ji0ceMaX68odeLRs0/jH84QJAhOlqo7o2a0MS5z6JkV/fcu49u+g0SydeZ+3EKPWm0ffSNh7VAWVa22n/XcTndtiXBuGxeMS8p7v4A4Xakyc94E/+fn1aKmYsYuhBnUHyDQX9GLz/l7JlDnNAVMFRfZNvUg8CWCSQfDSOh2Jtk822f77vuQnM2q7U9w2v27eGT9KUxfsI+f2PQNArPC7dEshglCBBDCDx5ahRvHbJ58ggcKm/jZpW+y8ZN/zvxUF5RIwdZv/m8mzTL/WDkbz5KY+zrs+cAHiPyYUx/YBwLueNN1ABg/lpBsBc96lO+Vi0DM6vUfRyRw4Wkn8/j23z3kd7L1sy2SdsQ9N/4y4naHpB8Q/8eIyAioT6vQwMKEukr9Vi1TRACmNyqDLagwtFbrhzz++F+xb//X+U+XNNhjmQgpqTn/m/vuP7SBNe1UsQ9TpgGljsRxh4mJl42UioZRPGuK4llTQ69R35l+fzf33f9+ANpt9b06VMZICiczsTYxzRJnnPHnTE68/LDb58iR4/jFCUtGmiu7CB2Hh0y9MFcMnKo2MIYFSnoOR4piS3UkLLQeJqm/kqJV5WW6QvHYmMOd0/DDugW8aPAiGTHW9QhMwRNRnTpglnZSLynvSU//Sd+pCpwG6lPRN8838irmmOAu4zw4ZODk4K53df0ezqw/cNTXIsUsqtyz213Pbg7oVrBJZ/wBULcXOWfN9w7aR69XZnlpDcvLq8DyOSu5g3hLQNHpwPrbOA+gDA2qCBkTCJfv8sonPS5bBpTosKO5lnsXtvHw8mYud/6dM8+8BwDHVnpGsbQAfIP1d5wJnM1+e4z99hj/vnw6W7r3MV1chC0dJJBO+LH3Cqq3qr+tOW+RB9ZvYuujexG7d2LXJLwYjCYY39zFBJBs28jfnvxS3nbvP9N49F+BoY9Ff5WSiZjmVoinY3roL4m2n1R5gL17D/MZZd7X3YPHNgP0qfPvI5te+KrLGHvjwKtVmxmYhNO215XGbaw0bqMugItULuzK0ldZOcx1TuEehmSA6mLx/b2sWf0LP2IvAyhSIYjjDnv3fumg/R0O6ewZ153l7LM+RaVy2mG3zZEjx/ENIaWUP3qzY4tms0mtVqPRaFCtHtrpf7SIo4j/9ZsfourNcovXYV14L2PTS2w+4yGWV2p884evAKDabxDHBtMLK1itLsKcYbpyEk51A7tnx5BAT4TcXY/ZZT5CQheJgRvA+j172D+zgXtOeQ2nPLGdDzS+yo7pNcSGQb3rY8Y+gb+EXH4U264TA3vrRfpuif3+GEFkUKgU2GefQmP5JEwRMNPfhxVDaLjE2rwZeTHF6i7K3UeodLU3BEEhDjCSNrVwP17iE7seIoqoW00Mo8Cc4xEagppONfUTuL+6lZZTpWGWMQT4podvuFhBDEg8fIpGj9WtPdhRAkKwuvUgQposLa2j365jiIRlo8hE0GaL5YOR8PkfP4dJb45Nrd28cuFuAkxuF6fxoHUy+5MNJImFD8wXSkjTIEoEFgHT1n7WdPdw8hMRYViEfoj1/ce5bP+9XHflRfzcRTNU5R46KzGmVAtb4VPf5caVKfavX0syrs6tWFhh/dQjbDJXEIBEEsYhctlD6MC3hajMD+MNvHGsgfYf06stYoQ2bld975qJza3xOl4zPkZZtzM3F3q0lvrZd0saPXrrv4dVjtPOZ8JYEtYmWH/+jx3+O7myjP/wIwz/kxRC4Jx0Etb4YPKs44wzO/vTh00gDcNl5ub+njgeHNOehpqxtKZeOORrUpTLJzM5efhj7HZ30Ok+ytTkkxPIA7Gw8G3a7QdHHrOsCqtW/XSWUXIg4rjL/L6vMTHx8szMmiNHjuMLR7p+n7DKiGlZWDX4jZe+BITgvza+xZa96+jzEH05xldf/FYKssuneCtB12XHv8zSbRU58xUX8/AdW5AteJP1af5g/c10g//Ov23azPse/TY/ue86Ppa8nXMetImDJ/i3SbWILGzaytvCq3GEIJCSj4//GlvGtvLEt07n4btVeSgqGFi9gAIBj55xFvd2tvHrF1Q5e/U6PnznD7l0Gk6+5fNIJF95yR7+/s1fZPIr7+Zj5ml8bOaXeJtxMj83cyN79lzHpk3vZfOm98BnfoJv3bLCXctrsnOf+Ln9vHr9O7G+9jtwymvhzV8A4B/e/SpmFx5iFjj5/PN5XfBJiPo0rvgkX/zc12kt7MerVPE7bWSS8M5PfIbq5BQP3XwjD93yPd72ofdge95B1xrg83c+zD82OvzGWbO8aJO6837pk3w+7/78nfzTPXv5lde8nF992cB0KeOYhy65lKTV5NG7T6H+k2/m3PUDdUhGEQ994Cv8eOtxNn7sNymcddahdn9YHNoFMYrzD/j7M7pMnvP0d2HbY6xb94sjj218+rsFoFjc+JTMo5OTr2By8hVH9RrTLLJ61RuP+r1y5Mhx/OGE7aYBOO+CC7NY8rlgDa72efQidTddoEu5fBr3f+08uvvU3Vt1agxX1+bb8Sa2GgXKTSXzF2WPmTjCi8oIS5kexld0uFesPCeBvust0COOO1z+znfj6U4Jsz8Y9S6X1Xv4wqYbqP23nngMgGYxxK+bTIxvhbf/A5NTql1zoTCT1efj1CxoFynbg64MAMtNMLp6EnJpMBOlUhzEtr9obROiPqy9iNqLfoH/8F/+OzObt9JvNZFJQqFSpTKhluGTX/RifvK9v3VYIgJw+UQVzxD82PihzZEH4vQ1ikH/cM/oxGZhmpR0K/m5+x+m4o3y6f6995K0WhjVKt7ppx/Re+XIkSNHjmOLE5qMVCYms6Cv3YuDEKVuoshB2YDzz7uOcBCPgFcqYek5MJ1kDb+y4SeYbiuiUkv6jMcJxbCMYaq7//qC6pAJktFqWIEuUdSiWK3xop9R9Xc9IBZhSMye8q/4WPRC7XDoK9/KHaessLW+VeUz2AUmL/pFABasylDAlD5op0jZGiRYAliewOjqKbXFwX391LhqyTx5yzRT25VawsvfD0JQHhvnTR/+CFsuVJ6Y1adsO2Q+xOFw1YYZHn7JWZxXK/3ojYEzVisT5Q93Nw56rqhHD5y77yHK7miponPTTQCULr4YYZoHvTZHjhw5cjz/cMKWaQDK45OUH72f/WNTLISriW1lLOwkakEfK67FssqIodq7UyxhugYECd14mlLnTsbbDkjJeNjEBCpBDWFOEAsotnW77tD7OgJsGWUhUBvPOR/+96fUk0IytnUFd16pGb3EwNAzYuwkxC2V+f1f/D3WlAdllylHfYz7g/BgMmKX8Owo21aYEstxoaGH6JQGXQ/bNlUpLf4ra8sG+EoV4aSBf8D2PF539TU8/oO7mdl09KFTtnHk5OX01UoZ2b7Qoe1HlN3BV9W+WBGi05Yep5gEwECR6fy7MtWWLrv0qI8vR44cOXIcG5zgysgElba6826XqsSOWsDbUg8lCyVJEmMMkRGvWMLReQ69pE68tBszhnJfMhY1SaRLMawihEmrJCj0u9lr0/v0iplGxav3c4ZmtyAF/ZUijp4/sn9xhVZb5ZhYMuKcK17LBbMXsKo8yKqY1GRkIYiwzAOVkRLhUPnEdGNMswAdHWhWGigjRqHCpvIytr+oHnj5b2ezZ7JtDJNN55xPsVZ/skv7tDFRdllVU8d9/97RUk1vahVzxTFsGcP378oeTzodut//PgClS3MykiNHjhzHC05oMlKqj1NprQDQLBSJXEVMmlLdlfd7IX63y/By7JZKuDph0pcG0ZIiDTVfMh42iORaXO05Wa7GuIGP0D6Ruq1IQ8VStCSOuyRJhOMNugmEkdCdd3EStd/du55g+wNKsXGIufRn33zQeUzq/bbihNjQZCQelGl6xUH0uOnEmEYBOppwFAeeEZwhP8faC+Gko+uYeKZxui7V3HtAqaYTxNw1pdJRe7osA9C57TYIQ+y1a3HW54PUcuTIkeN4wQlNRkzLoh4o5aLtekSOugNv6om9jaZPv60W9dTx4RZLlOopGZEkHQ9IqPt9xsImQbIOS0/N3V/vI5B4ehpuUSsiFWtQcojjNkk0KKNsevUTuGUnIyONdp+lBUUcZtetxbAOrqxVLRNHKxgN0rTLQZlmuVAnsFVSqOXFGKYH3bRMM9QL4g7NS3nZ+w9SRZ5rpKWae3ePKiPtfsRd02qWTed7g6yTbuoX0Z6SHDly5MhxfOCEJiMA41K5OfqujbQVMVkRdQCWGz7NldG7crdYojKhyIafQJSspmisUG7NMR426MVbEPqyzo0rQuD1lPHUEgMyYhiK0ERRi90PqQh6t+ZTXdfhsre8FkcPSAsMh1BPpj3nJYduhhVCZKWahh7yNmxgXbDHaBf1FFwnxjQ86GplZMgzkv3/mgtgy7FVRQDOWKNNrHtGP4OWH3L31FYSBP7DDxPtVyWnlJjkfpEcOXLkOL6QkxFtLTUcHRaWCJq61JGEMQ88rqakphqBWypRnVQeD19KwmQ9ZWOO2uJDuDKknaiunMDos1zpY7g2nvaN6OnxVC1jxGj6xH0/AKC8povAxnM24Oppwb7hEGkyUh87fEx3WqpZSQrZfgGwiyw4dVol9X6mG2NggR4AOFKmOfsX4OXXwM995pirIgBn6Pbeh/e16acdRShlpOWU2DOtSjGdm24inN+H//AjIATFoSnSOXLkyJHj+Y8TnoyM6TW3YCnCEPoOQTrlNJI8qMmIgoHluIzNKo+Hn0Ao11OS9zDb3gNAJ1kLQN/uIAVU163JTKw9nTVSNs0hMtJm1w/vVY+v6uDam/G7QVamiewikalKLEXn8K2qqTKylKhjT5I+SRKCU2K/M06npJURN8FMrbRuFYYmuuLVVCtv/fnht5itekyWXeJEjuSNtH1FpHZuVDkinX//Hp2blCribduGNTZ28M5y5MiRI8fzFic8GSlYJlYUUkWVAsLAITBVt4wIk0wZUQ+4IKE+rdUHwE/WUBKPMd1bIJEuvUQlrvYtlV8ytWkznq+6YRqRuruvWmbW9dJp7Wff42r4WXl1F8fYjN9pZ2QkNB0qa9VgE88+AjISDbaJ4zY4JRbsOnunFUkqTPQx0j7jYVXkeQghBOeurwNw187l7PGUjMyfrNJVOzfdNCjR5F00OXLkyHHc4YQnI8JxKXda1DIy4hKkJtFIsn9xsAgK4eL3ItziIGjLlxY1M2bSXyaSawgSnZxqdzGFybqt2/B8HXymu2oq1kAZ2fvgIyAlTjXCLsaYyQb6nTZuSkZiyUpX+UeKzuFjYSZtdUyLkcQwhko1tvKM3HnGi7Df8ybGT2lgpvWiYb/I8xQZGdm1kj3W6isy0jppG8J1ifbto/X1bwC5XyRHjhw5jkec8GQE26XSaWTKSBB4BHoAmovAjYfSS4VLvx0ihMDQnTF+IqlYE0zRJ5TrM8KxSq7n4y//OOtPPguv3xt5y7JpYGoyMveQGtteWa08HiJYg99pY8sQoXt4FjvqGApPooxMDWeNDAefOSUWnDEQgrG6QAgwY+2/KD2jU1WeFZy7TpVc7t65kj2WKiOFconi+WpSjOz3Ea5L4dxzn/NjzJEjR44cTw8nPBmJhUW53ciUkSAuZWWaUydLFJJB4JkQLv2OUiksR126QELBmmHVVJ0oWY+vRYcJd4ofW/9jhGOTWHHEMKpDysi+R+cAZV4NuyZxv0C/00YABc090iGuR+IZOYiMaGUEoI4qHRm6XPR8L9MAnLW2hiFg90qP+ab6LNpaGSl71ogSUrzgAgzXPeR+cuTIkSPH8xcnPBkpRB6VTnPgGYmKWZnm3NkKq/p7BxsPkRFbD2jrJ2AZq5ieritlRM+gKZTV83uDCN8ZHSCnyjRlIt9gRRszy6u6dOcLBN0efkeRhpI9+vE8qWdEd9MshCGWNcgakbqbBqAq1HuZoR6cdxyUaUquxckzilzdpdWRVBmpuNaIRyT3i+TIkSPH8YmnREY+8YlPsHHjRjzP4+KLL+bWW2897LbXXnstL3nJSxgbG2NsbIzLL7/8Sbd/rlHsu9S7vUwZ8aMCkakW9jPMBrWoRaQvkxBORka8otomkBLJWiZrBpEcKCOFquqA2esHtIcSUAEqpoFlVujsLYKE4riNXYro7CsQ9Hv02yqXpOSMfjxPpowM5tOMRsI3jQKBoY4lPUcz0GrPcVCmATh3vVJ27tql/DutIWXEPeUUrFWrwDAov/Qlx+wYc+TIkSPHU8dRk5EvfvGLXH311Xz4wx/mzjvv5Oyzz+aKK65g3759h9z+O9/5Dm9+85v59re/zU033cS6det41atexe7du5/2wT8TKPVsJvtRpoz0osFU2dIONeekYet8D+HRbysyUqioBd6XEMq1OI1dRHKWQJORypgqF+zxQ5qV0VbT1MDa3qtahMurlRLSnS8Q9nr0tTJS8UYNq0/eTaMNrGGEMURGFoQ6jnLUwYqVMmL4mowUjxcyUgcGvpG2rz6DsmshDIP1f/0p1n/2M7hbtx6jI8yRI0eOHE8HR01GPv7xj/POd76TK6+8km3btvHJT36SYrHIpz/96UNu//nPf55f+7Vf45xzzuHUU0/lU5/6FEmScMMNNzztg38mUA0LTPUTaqwA0NFD8jwBu269EYCWLnsMe0aySPgkBmz6cyXAoJ92zEyojpa9fshSfXTRr1omizs6rDyqsj8KMwsgBd39ShlJyzTVgpO9xrUMzCeZejuhyzSxhJ6pvCBR1GJBKpIyGS4TR0pxMXV3D6Xnv2cE4DxNRn7wRIMoTuj4yvNS1mTN3byZ0kUXHavDy5EjR44cTxOH7xU9BIIg4I477uCaa67JHjMMg8svv5ybhgaWPRm63S5hGDI+Pn7YbXzfx/cHXSzNZvOw2z4dJEnCWFxi1hdUUe/RShQZKcqEbmMF4ZWQliYFwqHf0X6FcR0JLwPAoReqsfaqm0ZkKa17/ZBOcWgAHXDzZz7J8o03ADZ2KaSyro0lVpNEBn63g99TZKFaHJCRwpOUaABsQzBmmSxHMU0xgYkalrc/UuRoKlgm0WTE6Ot01uPAMwKwebJMxbNo9SMenG+NeEZy5MiRI8fxj6NSRhYWFojjmJmZmZHHZ2ZmmJubO6J9/PZv/zarV6/m8ssvP+w2H/nIR6jVatmfdevWHc1hHjH8xQ4eNtMkWKgFbr+tFmhHB5Wd9ZKX8vJNSsEQQ2Uat6C272vDai+5gFhKEqnUizSldU8/UNHqaUsMsOe27yEMweTpS5zyxsew3ATXVCWG9tJitm2tVMheU3ySEk2KtKOmSR1Q6a4LoTrOyXCFOFYkx+y19U6PjzKNYQjOWVcHlIl12DOSI0eOHDmOfzyn3TQf/ehHue666/jKV76C53mH3e6aa66h0Whkf3bt2vWsHE9vl/KJWJ5SYboUWSgof4fRUs+deunLCLq6rDFUpuk21DH5MiUJbmZejQVUJ9X57dX+hqIczFbZespp/MyH3sPaF89jeerxonsqAK0FNfTNclxqQ8qI9yOUERiUahoMeUYCTUaCZZJYESwznRJ8nBhYAc4dIiOpZ6T0JCFwOXLkyJHj+MFRkZHJyUlM02R+fn7k8fn5eWZnZ5/0tR/72Mf46Ec/yte//nXOOuusJ93WdV2q1erIn2cDwV6lEMSuKls0qbKsB8rZvS7liUnWnLqNfleVN4a7aRZ3/VDtQ1pIrWQEOma94wl6gJQyIyNjOv/CQ/Lzv/P7TG/YMnIspdKZAJl51SuVKA+VIZ6skybFlDaxNqTyuMRRi/2Bev/JcJlYZ6YYiQSnMjqX5nmOtKPmth1L9EN1oQ80+ObIkSNHjuMTR0VGHMfh/PPPHzGfpmbUSy655LCv+2//7b/xh3/4h1x//fVccMEFT/1on2EEe9TCH9lKBWlQZ1n7O9ygz6mXvhRhGJmhFOHhd0KklDzx4J16L4JQKyJp+mrXNdjrhyyGcfbYlKfbax0bIQSmOWj3tawK5fIoOXFL5ZHF9snSV1OkZZrhyb1ZmSZYJtGTgM2Y40oVAbIyzc6lbvZYKfeM5MiRI8cLAkddprn66qu59tpr+Zu/+Rvuv/9+3vWud9HpdLjyyisBeNvb3jZicP2v//W/8sEPfpBPf/rTbNy4kbm5Oebm5mi328/cWTxFxPNKKQgdlV/RpEajoEhCSkaArEwjdBz84q7H6S4vIKW6Q/elWuQDqfbXdQV7/ZA5X4WLTdoW47qEUrEUqUhTUgGqlbNxD8gi8cplyt5gBk7hCEoSafDZsp7cG8VtFoMhz4gmI0YijzsyMlZy2DQ5aLv2bAPbPOEz+3LkyJHjBYGjvrV805vexP79+/nQhz7E3Nwc55xzDtdff31mat25cyeGMVgk/vIv/5IgCHjjG984sp8Pf/jD/N7v/d7TO/qnie2ntXnirkdxrAVAkZFEH3vNtpjedBJREBCliaXCIQoTtt+VqiIxYBDJ/cA6QtrAGF3XYI8f0E/U5V3t2oxpolA2FRkxDBchbKQMqdbOwSkMzKoAbrFEcUQZ+dELbxp8thQrEhNFLfZLRUamgiXd+QNmIo8b8+owzl1X57EFVTIru/aP2DpHjhw5chwveEo691VXXcVVV111yOe+853vjPx9x44dT+UtnhPM+0s8JvZymjawNqhlz61dtRohBL72iyAEhukhJTz+g7vVQ3qQXSiU58RHLfapMuLrTptVnk1dKyJVK01zFVhWhTBcolY9B8cbJSPeAWWaJ5vYmyIt0yzH6r3CsMFCosjIRLjCEyj/iBEff8oIwDnr63z5LhWWl/tFcuTIkeOFgxNa5240Gog4xiqojpbmEBnZtGkTAL4u0biFIl7ZRsqYPQ8p86rUl88X+7HFQ/hCJ6lqz0hqXl3lOtS15yMt0wCsXftWxsdfwtjYJZi2jWEOFli3XKYydPf/ZOmrKdIyzVJkYJplgiSkoYfijUfL2XZmwnFJRtIJvsCIuTdHjhw5chzfOKHJSKvVQsQRls4MGVZGpnUom99VBMMpFvFKNjLeTxT4eKUKoAhCB5MZ92p6ifp7qozs0Z6R1a7N2RWVO3JmeaCAbN70Hs4957OYpocQYqRUc7AycuTdNPvDmMnJV9BAdSFZMqGaDDw6Rnx8lmlOXVXB1cpSTkZy5MiR44WDE5qMnH322Yjk0MpIVSsYfkeVabxiCa9sk0Qq3G1i3WaEUAFnzUgt7N1ImVIPVkZsXjVZ457LTue9G0YD44bxZGTkaLppOnFCeeKK7Hwm8EnjUIQU6kM/DpUR2zQ4a606pzzwLEeOHDleODihyUiSJCPKSDcckIGqNppmZZpSWSsjKmOlOrUh23Z/sBF+8k9pR6qM0HEFe/xghIyAUi5SAnMoDPtG3FJ5ZMH9UXHwAGXTwE3n15QvoSUU4RgTfWL9uKmzUI5HMgKDvJHcM5IjR44cLxyc0GQkCAIMGWK5aoVOugOikHo7DizTpMpIaXx9tm0UmQRnvBUp1eXsugZLYcyuflqmGSSpPhnswigZcS0TR5cljkQZEUIMfCOJTVy6EIAqKyT6kzZjHYpyHJZpAP7DRet5xSlTvPmi9T964xw5cuTIcVzghCYjvu/jWEr5kInAaA8i2w9VprHdBJksqb+XVo/sa+GJgScjKejX6m6a2SNsQx1WRrySytSoagXgSDwjMCjVLAQRYfFs9Vr2EZuKaBmxPsfjVBnZOFniM1dexIUbDz9oMUeOHDlyHF844cmIbal5LSRFKp1W9lxFKxLDZZqwPwdILLdKHBdH9jX3yEr2/2OVgRIyZpkUjzCc60DPCAyMmkdSpgGYtBXxWQgj+rbqCCrIeTpF9frjXRnJkSNHjhwvPJzQZCQIAmxHz2uhSrnTzJ6rHlCmcYtFei2VceGVV9NrBSP7mtuhXisMwWxhQEaOVBUBcAoDguOWFRmp62F5R+qRmBpSRhZj9f81VpibVoP7srk09uEHFebIkSNHjhzPJU5oF+CaNWvYt6yUAsuoUe4qQmEjcQ0DKSUrc3sBlYjaXrwPANNZRa8VjuxrYZciLZZtZIZVYOT/fxQOpYy87/KtfP2+eS496ciUjOEyzaKeS1OlyUpdHYcZSyhNHPEx5ciRI0eOHM82Tmhl5NWvfjX1ulq8LWuMWk/5Q4qJ8lXc9g//hx3fvxOEYM1pp9OY3wGAMGfoNnW0uqW8GO1lpbDYrsnqIQJypOZVAMcr6n1aWI6aL/PyU6b5458+84hCz2AQfLYQRizouTQ1GtnzRkJeosmRI0eOHM8rnNBkBCDRUe6OM8FMaxlkwrTf4YF//y7/9oXPAvCKt/8yY6vW0FpUnTRxPJmVaSxXkQQ9Mw+naLHKGxCQp6KMuKXyk7YAPxnSMs3+IGS/JiOrnMFQPjORUJp6SvvOkSNHjhw5ng2c0GUaAAzdLePNMBM+wNv+v/8fa8slrt/9OADnv/anOO81r+Pxe+4GQBhVQt8hjhQZ8Yo2fifKdlco20wOl2m8oyAj3oCMPFVMpimsQcRCqEpJJ9W20d7/XSAv0+TIkSNHjucfTnhlBEuVV7ziDG6hyMziHOHjjxJHEVsvupSX/cd3ADC//REAhDkLEpJIeU2KtdEyTLHqjKghq49GGSmqMk3a1vtUkHpGdvR89CGydeol2fPGcTqxN0eOHDlyvHBxQpOROIowXTWxt1hePaJIrNp6Cq/5f/4zwlCXaO7RhwCwvVXZNrZrUh5zR/ZZHvMOMLAeuWdk3bYzmVq/kW0vfeXRn4zGlPaM9HXGSdUyGK+fRrGr1BvzOJ3YmyNHjhw5Xrg4ocs0fqedzaUpVdZQHleLtFep8obf+hC2MyAaqTJSqK6hr6JHKFQdKuOFkX1WJjwmbIu1nk0nSljvHTkZqUxM8rY/+Z9P55QYt0c/0knbBqfEmr19Hj6pTLkT556RHDly5MjxvMIJTUa6rSa2pxQD152iPKbmnpz5isspVgdD87rNBs39+wAoj6+n31V+kWLFpjo5mtdRmyxgCME3LziFUEoKRxh49kzBMgTjtslSqEjWpGOBYbJuHqYWlvD8JC/T5MiRI0eO5xVO6DJNrz2H0B2zjjOehY6F/mig2fyjDwMwtnotxeqglFOoOJTHR8lIqa7UlLptMeUcuV/kmUSawgqD7hphFyn4CQJyA2uOHDly5Hhe4YQmI53WHgCS0MYw3Ky1Nuz3Rrab02Rk9qStuKXBQl+oOpQOMLAWKseGgAwjNbECTKRlG2fIFJsrIzly5MiR43mEE5qM9LrzAMhIqRupMpLOo0mRmldnN2/BKw/IRrHiUKgcQEbKR+4RebYwTEay/7eHZunkBtYcOXLkyPE8wglNRvy+IiMkSjVIlZFgSBmRUmbm1ZmTTsYbVkYqDoUhcmIYAtM+9pd0asjEmpWKHE1GnDLYhUO8KkeOHDly5Dg2eEor5yc+8Qk2btyI53lcfPHF3HrrrU+6/Ze+9CVOPfVUPM/jzDPP5Gtf+9pTOthnGoG/CIAhlQ8kVUaC3kAZaS8t0llZRhgG0xs3HUBGbAzTwNQTfi332BMROEAZSYmJrcs0xdwvkiNHjhw5nl846tXzi1/8IldffTUf/vCHufPOOzn77LO54oor2Ldv3yG3/973vseb3/xm3vGOd3DXXXfxhje8gTe84Q3ce++9T/vgny7CeAkA06gDgwTUxV07ueHTn2T7XbfxxP3qOCfXrsd2PbzyYKEv6hJNqa4n6449PybhHrJMk3pG8rbeHDly5MjxPMNRk5GPf/zjvPOd7+TKK69k27ZtfPKTn6RYLPLpT3/6kNv/2Z/9Ga9+9av5zd/8TU477TT+8A//kPPOO4//+T+fXp7GM4E4XgHAsuoATK7fgFepEvp97v6Xf+QrH/19vvYXHwNg5qStAKPKSFWTEN1RU554fpCRqUN002RlmtwvkiNHjhw5nmc4KjISBAF33HEHl19++WAHhsHll1/OTTfddMjX3HTTTSPbA1xxxRWH3f65RCLaADiOWqCL1Rrv/J9/zU/9xu9y1uWvpjI5UBHWnX4WMEpGUmUkNbEO+0eOJZ68TJOTkRw5cuTI8fzCUYWeLSwsEMcxMzMzI4/PzMzwwAMPHPI1c3Nzh9x+bm7usO/j+z6+72d/bzabR3OYRwwp9JA8dzp7zPEKbLnwRWy58EVIKVl8YiftxQU2nHUuoGbPAJiWgVtUl69Uc/Vzo9HwxwozOo7eMwQ1SwepeDrErTJzmFflyJEjR44cxwbPywTWj3zkI/z+7//+s/4+nnUK/srj1E475ZDPCyGYXLeByXUbssfKYx4vesNmilUHYQgAznj5GuIo4fSXrH7Wj/lIsM5zeP+mWWZdGyHUMXLhOyDqw/m/eEyPLUeOHDly5DgQR0VGJicnMU2T+fn5kcfn5+eZnZ095GtmZ2ePanuAa665hquvvjr7e7PZZN26dUdzqEeEV7z+/31Krzv/1RtH/l6fLvKy/3BoQnOs8L6NB1zfiZPgJz9+bA4mR44cOXLkeBIclWfEcRzOP/98brjhhuyxJEm44YYbuOSSSw75mksuuWRke4BvfOMbh90ewHVdqtXqyJ8cOXLkyJEjxwsTR12mufrqq3n729/OBRdcwEUXXcSf/umf0ul0uPLKKwF429vexpo1a/jIRz4CwHvf+15e9rKX8d//+3/nta99Lddddx233347f/VXf/XMnkmOHDly5MiR47jEUZORN73pTezfv58PfehDzM3Ncc4553D99ddnJtWdO3diGAPB5dJLL+ULX/gCv/u7v8vv/M7vsHXrVr761a9yxhlnPHNnkSNHjhw5cuQ4biGklPJYH8SPQrPZpFar0Wg08pJNjhw5cuTIcZzgSNfv52U3zYFI+dKz1eKbI0eOHDly5Hjmka7bP0r3OC7ISKvVAnhWOmpy5MiRI0eOHM8uWq0WtVrtsM8fF2WaJEnYs2cPlUplkJvxDCBtGd61a1de/nmWkV/r5w75tX5ukV/v5w75tX7u8ExdayklrVaL1atXj/hJD8RxoYwYhsHatWuftf3n7cPPHfJr/dwhv9bPLfLr/dwhv9bPHZ6Ja/1kikiK58fM+xw5cuTIkSPHCYucjOTIkSNHjhw5jilOaDLiui4f/vCHcd3nx4C7FzLya/3cIb/Wzy3y6/3cIb/Wzx2e62t9XBhYc+TIkSNHjhwvXJzQykiOHDly5MiR49gjJyM5cuTIkSNHjmOKnIzkyJEjR44cOY4pcjKSI0eOHDly5DimOKHJyCc+8Qk2btyI53lcfPHF3Hrrrcf6kI57fOQjH+HCCy+kUqkwPT3NG97wBh588MGRbfr9Pu9+97uZmJigXC7zsz/7s8zPzx+jI35h4KMf/ShCCN73vvdlj+XX+ZnF7t27+Y//8T8yMTFBoVDgzDPP5Pbbb8+el1LyoQ99iFWrVlEoFLj88st5+OGHj+ERH5+I45gPfvCDbNq0iUKhwEknncQf/uEfjsw2ya/1U8O//uu/8rrXvY7Vq1cjhOCrX/3qyPNHcl2XlpZ4y1veQrVapV6v8453vIN2u/30D06eoLjuuuuk4zjy05/+tPzhD38o3/nOd8p6vS7n5+eP9aEd17jiiivkZz7zGXnvvffKu+++W/7ET/yEXL9+vWy329k2v/qrvyrXrVsnb7jhBnn77bfLF73oRfLSSy89hkd9fOPWW2+VGzdulGeddZZ873vfmz2eX+dnDktLS3LDhg3yF3/xF+Utt9wit2/fLv/lX/5FPvLII9k2H/3oR2WtVpNf/epX5fe//335+te/Xm7atEn2er1jeOTHH/7oj/5ITkxMyH/8x3+Ujz32mPzSl74ky+Wy/LM/+7Nsm/xaPzV87Wtfkx/4wAfkl7/8ZQnIr3zlKyPPH8l1ffWrXy3PPvtsefPNN8t/+7d/k1u2bJFvfvObn/axnbBk5KKLLpLvfve7s7/HcSxXr14tP/KRjxzDo3rhYd++fRKQ3/3ud6WUUq6srEjbtuWXvvSlbJv7779fAvKmm246Vod53KLVasmtW7fKb3zjG/JlL3tZRkby6/zM4rd/+7fli1/84sM+nySJnJ2dlX/yJ3+SPbaysiJd15V/+7d/+1wc4gsGr33ta+Uv/dIvjTz2Mz/zM/Itb3mLlDK/1s8UDiQjR3Jd77vvPgnI2267Ldvmn//5n6UQQu7evftpHc8JWaYJgoA77riDyy+/PHvMMAwuv/xybrrppmN4ZC88NBoNAMbHxwG44447CMNw5NqfeuqprF+/Pr/2TwHvfve7ee1rXztyPSG/zs80/uEf/oELLriAn/u5n2N6eppzzz2Xa6+9Nnv+scceY25ubuR612o1Lr744vx6HyUuvfRSbrjhBh566CEAvv/973PjjTfymte8Bsiv9bOFI7muN910E/V6nQsuuCDb5vLLL8cwDG655Zan9f7HxaC8ZxoLCwvEcczMzMzI4zMzMzzwwAPH6KheeEiShPe9731cdtllnHHGGQDMzc3hOA71en1k25mZGebm5o7BUR6/uO6667jzzju57bbbDnouv87PLLZv385f/uVfcvXVV/M7v/M73HbbbbznPe/BcRze/va3Z9f0UL8p+fU+Orz//e+n2Wxy6qmnYpomcRzzR3/0R7zlLW8ByK/1s4Qjua5zc3NMT0+PPG9ZFuPj40/72p+QZCTHc4N3v/vd3Hvvvdx4443H+lBecNi1axfvfe97+cY3voHnecf6cF7wSJKECy64gD/+4z8G4Nxzz+Xee+/lk5/8JG9/+9uP8dG9sPB3f/d3fP7zn+cLX/gCp59+OnfffTfve9/7WL16dX6tX8A4Ics0k5OTmKZ5UGfB/Pw8s7Ozx+ioXli46qqr+Md//Ee+/e1vs3bt2uzx2dlZgiBgZWVlZPv82h8d7rjjDvbt28d5552HZVlYlsV3v/td/vzP/xzLspiZmcmv8zOIVatWsW3btpHHTjvtNHbu3AmQXdP8N+Xp4zd/8zd5//vfzy/8wi9w5pln8ta3vpVf//Vf5yMf+QiQX+tnC0dyXWdnZ9m3b9/I81EUsbS09LSv/QlJRhzH4fzzz+eGG27IHkuShBtuuIFLLrnkGB7Z8Q8pJVdddRVf+cpX+Na3vsWmTZtGnj///POxbXvk2j/44IPs3Lkzv/ZHgVe+8pXcc8893H333dmfCy64gLe85S3Z/+fX+ZnDZZdddlCL+kMPPcSGDRsA2LRpE7OzsyPXu9lscsstt+TX+yjR7XYxjNGlyTRNkiQB8mv9bOFIrusll1zCysoKd9xxR7bNt771LZIk4eKLL356B/C07K/HMa677jrpuq787Gc/K++77z75y7/8y7Jer8u5ubljfWjHNd71rnfJWq0mv/Od78i9e/dmf7rdbrbNr/7qr8r169fLb33rW/L222+Xl1xyibzkkkuO4VG/MDDcTSNlfp2fSdx6663Ssiz5R3/0R/Lhhx+Wn//852WxWJSf+9znsm0++tGPynq9Lv/+7/9e/uAHP5A/9VM/lbebPgW8/e1vl2vWrMlae7/85S/LyclJ+Vu/9VvZNvm1fmpotVryrrvuknfddZcE5Mc//nF51113yccff1xKeWTX9dWvfrU899xz5S233CJvvPFGuXXr1ry19+niL/7iL+T69eul4zjyoosukjfffPOxPqTjHsAh/3zmM5/Jtun1evLXfu3X5NjYmCwWi/Knf/qn5d69e4/dQb9AcCAZya/zM4v/+3//rzzjjDOk67ry1FNPlX/1V3818nySJPKDH/ygnJmZka7ryle+8pXywQcfPEZHe/yi2WzK9773vXL9+vXS8zy5efNm+YEPfED6vp9tk1/rp4Zvf/vbh/x9fvvb3y6lPLLruri4KN/85jfLcrksq9WqvPLKK2Wr1XraxyakHIq1y5EjR44cOXLkeI5xQnpGcuTIkSNHjhzPH+RkJEeOHDly5MhxTJGTkRw5cuTIkSPHMUVORnLkyJEjR44cxxQ5GcmRI0eOHDlyHFPkZCRHjhw5cuTIcUyRk5EcOXLkyJEjxzFFTkZy5MiRI0eOHMcUORnJkSNHjhw5chxT5GQkR44cOXLkyHFMkZORHDly5MiRI8cxRU5GcuTIkSNHjhzHFP9/IJc9VAqaI0kAAAAASUVORK5CYII=", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "iris = pd.read_csv(\"./result/iris/05-26-07-03_100_100_0.5_1.5_0.75.csv\", header=None)\n", + "print(iris.shape)\n", + "print(iris.head())\n", + "\n", + "loss = []\n", + "acc = []\n", + "for i in range(len(iris.iloc[0])):\n", + " if i % 2 == 0:\n", + " loss.append(iris[i])\n", + " else:\n", + " acc.append(iris[i])\n", + "\n", + "print(len(loss))\n", + "print(len(acc))\n", + "\n", + "plt.subplot(2,1,1)\n", + "for i in range(len(loss)):\n", + " plt.plot(loss[i], label=f\"loss_{i}\")\n", + "\n", + "plt.subplot(2,1,2)\n", + "for i in range(len(acc)):\n", + " plt.plot(acc[i], label=f\"acc_{i}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-26 03:53:32.688348: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-05-26 03:53:32.796903: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2023-05-26 03:53:33.196478: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.2/lib64:/usr/local/TensorRT/lib:/usr/local/cuda-11.2/lib64:/usr/local/TensorRT/lib:\n", + "2023-05-26 03:53:33.196616: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.2/lib64:/usr/local/TensorRT/lib:/usr/local/cuda-11.2/lib64:/usr/local/TensorRT/lib:\n", + "2023-05-26 03:53:33.196622: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\n", + "\n", + "import numpy as np\n", + "\n", + "def make_model():\n", + " model = Sequential()\n", + " model.add(Conv2D(32, kernel_size=(5, 5), activation='relu', input_shape=(28,28,1)))\n", + " model.add(MaxPooling2D(pool_size=(3, 3)))\n", + " model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n", + " model.add(MaxPooling2D(pool_size=(2, 2)))\n", + " model.add(Dropout(0.25))\n", + " model.add(Flatten())\n", + " model.add(Dense(128, activation='relu'))\n", + " model.add(Dense(10, activation='softmax'))\n", + "\n", + " # model.summary()\n", + "\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-26 03:53:33.924839: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-26 03:53:33.928891: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-26 03:53:33.929032: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-26 03:53:33.929450: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-05-26 03:53:33.929902: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-26 03:53:33.930018: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-26 03:53:33.930117: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-26 03:53:34.287172: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-26 03:53:34.287322: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-26 03:53:34.287430: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-26 03:53:34.287524: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10109 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:09:00.0, compute capability: 8.6\n" + ] + } + ], + "source": [ + "model = make_model()\n", + "# json_ = model.to_json()\n", + "# print(json_)\n", + "# for layer in model.get_weights():\n", + " # print(layer.shape)\n", + "weight = model.get_weights()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 ~ 800\n", + "(5, 5, 1, 32)\n", + "800 ~ 832\n", + "(32,)\n", + "832 ~ 19264\n", + "(3, 3, 32, 64)\n", + "19264 ~ 19328\n", + "(64,)\n", + "19328 ~ 93056\n", + "(576, 128)\n", + "93056 ~ 93184\n", + "(128,)\n", + "93184 ~ 94464\n", + "(128, 10)\n", + "94464 ~ 94474\n", + "(10,)\n", + "[800, 32, 18432, 64, 73728, 128, 1280, 10]\n", + "[(5, 5, 1, 32), (32,), (3, 3, 32, 64), (64,), (576, 128), (128,), (128, 10), (10,)]\n" + ] + } + ], + "source": [ + "from time import time\n", + "import cupy as cp\n", + "\n", + "def encode(weights):\n", + " w_gpu = cp.array([])\n", + " lenght = []\n", + " shape = []\n", + " for layer in weights:\n", + " shape.append(layer.shape)\n", + " w_ = layer.reshape(-1)\n", + " lenght.append(len(w_))\n", + " w_gpu = cp.append(w_gpu, w_)\n", + " \n", + " return w_gpu, shape, lenght\n", + "\n", + "def decode(weight, shape, lenght):\n", + " weights = []\n", + " start = 0\n", + " for i in range(len(shape)):\n", + " end = start + lenght[i]\n", + " print(f\"{start} ~ {end}\")\n", + " print(f\"{shape[i]}\")\n", + " w_ = weight[start:end]\n", + " w_ = w_.reshape(shape[i])\n", + " weights.append(w_)\n", + " start = end\n", + "\n", + " return weights\n", + "\n", + "w = 0.8\n", + "v,_,_ = encode(weight)\n", + "c0 = 0.5\n", + "c1 = 1.5\n", + "r0 = 0.2\n", + "r1 = 0.8\n", + "p_best,_,_ = encode(weight)\n", + "g_best,_,_ = encode(weight)\n", + "layer,shape,leng = encode(weight)\n", + "\n", + "# new_v = w*v[i]\n", + "# new_v = new_v + c0*r0*(p_best[i] - layer)\n", + "# new_v = new_v + c1*r1*(self.g_best[i] - layer)\n", + "\n", + "start = time()\n", + "new_velocity = w * v + c0 * r0 * (p_best - layer) + c1 * r1 * (g_best - layer)\n", + "\n", + "# print(new_velocity)\n", + "\n", + "we2 = decode(new_velocity, shape, leng)\n", + "# print(we2)\n", + "\n", + "\n", + "# # s= [1,2]\n", + "# print(w)\n", + "print(leng)\n", + "print(shape)\n", + "\n", + "# w2 = w\n", + "# c1 = c\n", + "\n", + "# tf_start = time()\n", + "# w3 = tf.multiply(w2, w)\n", + "# tf_end = time()\n", + "# mul_start = time()\n", + "# w4 = w2 * w\n", + "# mul_end = time()\n", + "# cuda_start = time()\n", + "# w5 = c1 * c\n", + "# cuda_end = time()\n", + "\n", + "# print(f\"tf 연산 > {tf_end-tf_start} | {w3}\")\n", + "# print(f\"곱셈 연산 > {mul_end-mul_start} | {w4}\")\n", + "# print(f\"cuda 연산 > {cuda_end-cuda_start} | {w5}\")\n", + "\n", + "# for i in range(len(w)):\n", + "# if w[i] != w2[i]:\n", + "# print(\"not same\")\n", + "# break\n", + "# else:\n", + "# print(\"same\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pso", + "language": "python", + "name": "pso" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/xor.ipynb b/xor.ipynb index 14921c7..a6d9756 100644 --- a/xor.ipynb +++ b/xor.ipynb @@ -11,35 +11,70 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-05-24 15:30:18.194977: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" + "2023-05-26 10:26:11.173286: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "/home/pieroot/miniconda3/envs/pso/lib/python3.8/site-packages/cupy/_environment.py:445: UserWarning: \n", + "--------------------------------------------------------------------------------\n", + "\n", + " CuPy may not function correctly because multiple CuPy packages are installed\n", + " in your environment:\n", + "\n", + " cupy, cupy-cuda11x\n", + "\n", + " Follow these steps to resolve this issue:\n", + "\n", + " 1. For all packages listed above, run the following command to remove all\n", + " existing CuPy installations:\n", + "\n", + " $ pip uninstall \n", + "\n", + " If you previously installed CuPy via conda, also run the following:\n", + "\n", + " $ conda uninstall cupy\n", + "\n", + " 2. Install the appropriate CuPy package.\n", + " Refer to the Installation Guide for detailed instructions.\n", + "\n", + " https://docs.cupy.dev/en/stable/install.html\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\n", + " warnings.warn(f'''\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2.10.0\n" + "2.10.0\n", + "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n" ] } ], "source": [ "import os\n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", - "import tensorflow as tf\n", - "# tf.random.set_seed(777) # for reproducibility\n", "\n", - "from pso_tf import PSO\n", + "import tensorflow as tf\n", + "tf.random.set_seed(777) # for reproducibility\n", + "\n", + "# from pso_tf import PSO\n", + "from pso import Optimizer\n", "from tensorflow import keras\n", "\n", - "print(tf.__version__)\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "from tqdm import tqdm\n", "\n", "from tensorflow import keras\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras import layers\n", "\n", - "import matplotlib.pyplot as plt\n", + "from datetime import datetime\n", + "\n", + "import json\n", + "\n", + "print(tf.__version__)\n", + "print(tf.config.list_physical_devices())\n", "\n", "def get_data():\n", " x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n", @@ -53,12 +88,6 @@ "\n", " model = Sequential(leyer)\n", "\n", - " sgd = keras.optimizers.SGD(lr=0.1, momentum=1, decay=1e-05, nesterov=True)\n", - " # adam = keras.optimizers.Adam(lr=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.)\n", - " model.compile(loss='mse', optimizer=sgd, metrics=['accuracy'])\n", - "\n", - " print(model.summary())\n", - "\n", " return model" ] }, @@ -67,357 +96,1442 @@ "execution_count": 2, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "epoch 0/99: 4%|4 | 4/100 [00:00<00:18, 5.11it/s]" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " dense (Dense) (None, 2) 6 \n", - " \n", - " dense_1 (Dense) (None, 1) 3 \n", - " \n", - "=================================================================\n", - "Total params: 9\n", - "Trainable params: 9\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" + "WARNING:tensorflow:5 out of the last 5 calls to .test_function at 0x7f8a2a586e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/pieroot/miniconda3/envs/pso/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/gradient_descent.py:111: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n", - " super().__init__(name, **kwargs)\n" + "epoch 0/99: 5%|5 | 5/100 [00:01<00:15, 6.06it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "None\n" + "WARNING:tensorflow:6 out of the last 6 calls to .test_function at 0x7f8a2a509280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "init particles position: 100%|██████████| 15/15 [00:00<00:00, 82.69it/s]\n", - "init velocities: 100%|██████████| 15/15 [00:00<00:00, 39297.04it/s]\n", - "Iter 0/20: 27%|##6 | 4/15 [00:00<00:01, 5.80it/s]" + "epoch 0/99: 100%|##########| 100/100 [00:12<00:00, 8.33it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:5 out of the last 5 calls to .test_function at 0x7f3185f88310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "loss avg : 0.707333293557167 | acc avg : 0.5 | Best score : 0.6307240724563599\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Iter 0/20: 33%|###3 | 5/15 [00:01<00:01, 6.72it/s]" + "epoch 1/99: 100%|##########| 100/100 [00:03<00:00, 30.91it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:6 out of the last 6 calls to .test_function at 0x7f3185f885e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + "loss avg : 0.7328412693738937 | acc avg : 0.505 | Best score : 0.6244710087776184\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Iter 0/20: 100%|##########| 15/15 [00:02<00:00, 7.16it/s]\n" + "epoch 2/99: 100%|##########| 100/100 [00:03<00:00, 30.94it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "loss avg : 0.01894655227661133 | acc avg : 0.03333333333333333 | best score : 0.5\n" + "loss avg : 0.7291719603538513 | acc avg : 0.5075 | Best score : 0.621765673160553\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Iter 1/20: 100%|##########| 15/15 [00:01<00:00, 9.30it/s]\n" + "epoch 3/99: 100%|##########| 100/100 [00:03<00:00, 30.73it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "loss avg : 0.016662003596623738 | acc avg : 0.016666666666666666 | best score : 0.5\n" + "loss avg : 0.5099389508366585 | acc avg : 0.7175 | Best score : 0.3762193024158478\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Iter 2/20: 100%|##########| 15/15 [00:01<00:00, 8.80it/s]\n" + "epoch 4/99: 100%|##########| 100/100 [00:03<00:00, 31.01it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "loss avg : 0.02029351592063904 | acc avg : 0.03333333333333333 | best score : 0.75\n" + "loss avg : 0.4717184057831764 | acc avg : 0.7525 | Best score : 0.37326303124427795\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Iter 3/20: 100%|##########| 15/15 [00:01<00:00, 9.08it/s]\n" + "epoch 5/99: 100%|##########| 100/100 [00:03<00:00, 31.35it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "loss avg : 0.026707116762797037 | acc avg : 0.03333333333333333 | best score : 0.75\n" + "loss avg : 0.5643444469571114 | acc avg : 0.675 | Best score : 0.37197786569595337\n" ] }, { 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[ "\n" ] - } - ], - "source": [ - "'''\n", - "optimizer parameter\n", - "'''\n", - "lr = 0.1\n", - "momentun = 0.8\n", - "decay = 1e-04\n", - "nestrov = True\n", - "\n", - "'''\n", - "pso parameter\n", - "'''\n", - "n_particles = 30\n", - "maxiter = 20\n", - "# epochs = 1\n", - "w = 0.75\n", - "c0 = 0.5\n", - "c1 = 1.5\n", - "\n", - "x, y = get_data()\n", - "x_test = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n", - "y_test = np.array([[0], [1], [1], [0]])\n", - "\n", - "model = make_model()\n", - "\n", - "loss = keras.losses.MeanSquaredError()\n", - "# optimizer = keras.optimizers.SGD(lr=0.1, momentum=0.9, decay=1e-05, nesterov=True)\n", - "\n", - "\n", - "pso_xor = PSO(model=model, loss_method=loss, n_particles=15)\n", - "\n", - "best_weights, score = pso_xor.optimize(x, y, x_test, y_test, maxiter=maxiter, c0=c0, c1=c1, w=w)\n", - "\n", - "model.set_weights(best_weights)\n", - "\n", - "y_pred = model.predict(x_test)\n", - "print(y_pred)\n", - "print(y_test)\n", - "\n", - "history = pso_xor.global_history()\n", - "\n", - "\n", - "\n", - "# print(f\"history > {history}\")\n", - "# print(f\"score > {score}\")\n", - "# plt.plot(history)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'acc history')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" + "" ] }, + "execution_count": 2, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "all_loss, all_acc = pso_xor.all_history()\n", - "# print(np.shape(all_))\n", - "# print(all_[0][0][1])\n", - "# loss_ = [[]*len(all_[0])]*len(all_)\n", - "# acc_ = [[]*len(all_[0])]*len(all_)\n", - "# for al_index in range(len(all_)):\n", - " # for i in all_[al_index]:\n", - " # loss_[al_index].append((i[0]))\n", - " # acc_[al_index] = (i[1])\n", - " # acc_.append(i[1])\n", - " # print(particle)\n", - " # loss_.append(particle[0])\n", + "def fit(x, y, model:keras.models = make_model(), loss_method=\"binary_crossentropy\", n_particles=100, maxiter=50, c0=0.5, c1=1.5, w=0.75,renewal=\"acc\"):\n", + " x, y = get_data()\n", + " x_test = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n", + " y_test = np.array([[0], [1], [1], [0]])\n", "\n", - "# print(np.shape(all_loss))\n", - "plt.subplot(2,1,1)\n", - "for layer in all_loss:\n", - " plt.plot(layer)\n", - "plt.title('loss history')\n", + " model = make_model()\n", "\n", - "plt.subplot(2,1,2)\n", - "for layer in all_acc:\n", - " plt.plot(layer)\n", - "plt.title('acc history')\n", - "# plt.plot(all_loss)\n", + " # loss = loss_method\n", + " day = datetime.now().strftime(\"%m-%d-%H-%M\")\n", + " pso_xor = Optimizer(model=model, loss=loss_method, n_particles=n_particles, c0=c0, c1=c1, w=w)\n", "\n", - "# plt.plot(all_acc)\n", + " weight, score = pso_xor.fit(x, y, epochs=maxiter, save=True, save_path=\"./result/xor\", renewal=renewal)\n", + " # pso_xor = PSO(model=model, loss_method=loss, n_particles=n_particles)\n", "\n", - " # plt.plot(all_[i])\n", - " # for layer in all_[i]:\n", - " # print(layer[0])\n", - " # plt.plot(layer[1])\n", - " # for j in range(len(all_[i])):\n", + " # best_weights, score = pso_xor.optimize(x, y, maxiter=maxiter, c0=c0, c1=c1, w=w)\n", + "\n", + " model.set_weights(weight)\n", + "\n", + " y_pred = model.predict(x_test)\n", + " print(f\"추론 > {y_pred}\")\n", + " print(f\"실 데이터 > {y_test}\")\n", + "\n", + " # score_ = model.evaluate(x_test, y_test, verbose=2)\n", + " print(f\"score > {score}\")\n", + " \n", + " # pso_xor.plot_history()\n", + " \n", + " # history = pso_xor.global_history()\n", + " json_data = {\n", + " \"best score\": score,\n", + " \"epoch\": maxiter,\n", + " \"n_particles\": n_particles,\n", + " \"c0\": c0,\n", + " \"c1\": c1,\n", + " \"w\": w,\n", + " \"loss_method\": loss_method\n", + " }\n", " \n", - " # print(all_[i][j][0])\n", - " # plt.plot(all_[i][j][1])\n", - " # plt.plot(all_[i])\n", - " # print(f\"epoch {i} > {all_[i]}\")\n", - "# print(np.shape(all_))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 0s 12ms/step\n", - "[[0.51426786]\n", - " [0.49928975]\n", - " [0.49840933]\n", - " [0.48371843]]\n", - "[[0]\n", - " [1]\n", - " [1]\n", - " [0]]\n" - ] - } - ], - "source": [ - "x_test = np.array([[0, 1], [0, 0], [1, 1], [1, 0]])\n", - "y_pred = model.predict(x_test)\n", - "print(y_pred)\n", - "print(y_test)" + " with open(f\"./result/xor/{day}_{loss_method}_{n_particles}_{maxiter}.json\", \"w\") as f:\n", + " json.dump(json_data, f, indent=4)\n", + " \n", + " return pso_xor\n", + "\n", + "fit(*get_data(), make_model(), n_particles=100, maxiter=100, c0=0.5, c1=1.5, w=0.65, renewal=\"loss\")\n", + "\n", + "# pso_=fit(*get_data(), make_model(), n_particles=10, maxiter=10, c0=0.5, c1=1.5, w=0.75)\n", + "# print(f\"history > {history}\")\n", + "# print(f\"score > {score}\")\n", + "# plt.plot(history)\n" ] }, { @@ -578,56 +1611,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predicted_result = model.predict(x_test)\n", - "predicted_labels = np.argmax(predicted_result, axis=1)\n", - "not_correct = []\n", - "for i in range(len(y_test)):\n", - " if predicted_labels[i] != y_test[i]:\n", - " not_correct.append(i)\n", - " # print(f\"추론 > {predicted_labels[i]} | 정답 > {y_test[i]}\")\n", - " \n", - "print(f\"틀린 것 갯수 > {len(not_correct)}\")\n", - "for i in range(3):\n", - " plt.imshow(x_test[not_correct[i]].reshape(28,28), cmap='Greys')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "all__ = pso_xor.all_cost()\n", - "\n", - "def plot_history(history):\n", - " fig, loss_ax = plt.subplots()\n", - " acc_ax = loss_ax.twinx()\n", - "\n", - " loss_ax.plot(hist.history['loss'], 'y', label='train loss')\n", - " loss_ax.plot(hist.history['val_loss'], 'r', label='val loss')\n", - " loss_ax.set_xlabel('epoch')\n", - " loss_ax.set_ylabel('loss')\n", - " loss_ax.legend(loc='upper left')\n", - "\n", - " acc_ax.plot(hist.history['accuracy'], 'b', label='train acc')\n", - " acc_ax.plot(hist.history['val_accuracy'], 'g', label='val acc')\n", - " acc_ax.set_ylabel('accuracy')\n", - " acc_ax.legend(loc='upper right')\n", - "\n", - " plt.show()\n", - "hist = test()\n", - "plot_history(hist)" - ] - }, { "cell_type": "code", "execution_count": null,