From 6361618c8ee2f11c0482dcaed9a8fec28fd7a2b9 Mon Sep 17 00:00:00 2001 From: Wolves Date: Sat, 28 Dec 2024 12:50:41 +0800 Subject: [PATCH] routine --- lab/1_liner-regression-single.ipynb | 209 + linear regression/1.py | 310 - linear regression/data1.csv | 100 - linear regression/liner regression.ipynb | 5234 ----------------- linear regression/m1.pth | Bin 1402 -> 0 bytes linear regression/np_genPoints.py | 30 - linear regression/plt_print.py | 23 - linear regression/print1.png | Bin 34941 -> 0 bytes test/outputtest.py | 17 + ...1_W1_Lab02_Model_Representation_Soln.ipynb | 126 +- .../lab_utils_common.cpython-37.pyc | Bin 0 -> 3192 bytes .../lab_utils_multi.cpython-37.pyc | Bin 0 -> 18028 bytes .../__pycache__/lab_utils_uni.cpython-37.pyc | Bin 0 -> 25040 bytes 13 files changed, 289 insertions(+), 5760 deletions(-) create mode 100644 lab/1_liner-regression-single.ipynb delete mode 100644 linear regression/1.py delete mode 100644 linear regression/data1.csv delete mode 100644 linear regression/liner regression.ipynb delete mode 100644 linear regression/m1.pth delete mode 100644 linear regression/np_genPoints.py delete mode 100644 linear regression/plt_print.py delete mode 100644 linear regression/print1.png create mode 100644 test/outputtest.py create mode 100644 week2/__pycache__/lab_utils_common.cpython-37.pyc create mode 100644 week2/__pycache__/lab_utils_multi.cpython-37.pyc create mode 100644 week2/__pycache__/lab_utils_uni.cpython-37.pyc diff --git a/lab/1_liner-regression-single.ipynb b/lab/1_liner-regression-single.ipynb new file mode 100644 index 0000000..a0815a2 --- /dev/null +++ b/lab/1_liner-regression-single.ipynb @@ -0,0 +1,209 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# 引入库\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/wolves/Downloads/project/python/pt/lab\n" + ] + } + ], + "source": [ + "# 检查os位置\n", + "print(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + " # 生成数据\n", + " def generate_data():\n", + " w = 1.35\n", + " b = 2.89\n", + " x_min = 0\n", + " x_max = 10\n", + " x = np.linspace(x_min, x_max, 100)\n", + " y = w * x + b\n", + " y += np.random.normal(scale=0.5, size=y.shape)\n", + " data = np.column_stack((x, y))\n", + " return data\n", + "\n", + " # 保存数据\n", + " def save_data(filename, data):\n", + " np.savetxt(filename, data, delimiter=',')\n", + " print(f\"{filename} 已成功创建并写入数据。\")\n", + "\n", + " # 生成并保存数据\n", + " data = generate_data()\n", + " #save_data('./1_data.txt', data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# 读取数据\n", + "#points = np.genfromtxt(\"./1_data.txt\", delimiter=',')\n", + "\n", + "points = data\n", + " \n", + "x = points[:, 0]\n", + "y = points[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "损失函数: \n", + "$$J(w,b) = \\frac{1}{2m} \\sum_{i=1}^{m} (y_{w,b}(x^{(i)}) - y^{(i)})^2$$\n", + "\n", + "梯度下降：\n", + "\n", + "分别对w和b求偏导数，然后更新w和b\n", + "$$\n", + "w = w - \\alpha\\cdot\\frac{\\partial J(w,b)}{\\partial w}\n", + "$$\n", + "\n", + "$$\n", + "b = b - \\alpha\\cdot\\frac{\\partial J(w,b)}{\\partial b}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# 定义损失函数\n", + "def compute_loss(w,b):\n", + " return np.sum((y-w*x-b)**2)/(2*len(x))\n", + "\n", + "# 等效\n", + "def compute_loss_equivalent(w,b):\n", + " sum = 0\n", + " for i in range(len(x)):\n", + " sum += (y[i] - (w*x[i]+b))**2\n", + " return sum/(2*len(x))\n", + "\n", + "# 定义梯度下降\n", + "def gradient_descent(w,b,alpha,num_iter):\n", + " m = len(x)\n", + " for _ in range(num_iter):\n", + " # 计算梯度\n", + " dw = -np.sum(x*(y-w*x-b))/m\n", + " db = -np.sum(y-w*x-b)/m\n", + " # 更新w和b\n", + " w = w - alpha*dw\n", + " b = b - alpha*db\n", + " return w,b" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w: 1.368169058216238\n", + "b: 2.770596744047831\n", + "loss: 0.1375443007342851\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 主函数\n", + "if __name__ == \"__main__\":\n", + " # 初始化w和b\n", + " w,b = 0,0\n", + " # 设置学习率\n", + " alpha = 0.01\n", + " # 设置迭代次数\n", + " num_iter = 1000\n", + " # 进行梯度下降\n", + " w,b = gradient_descent(w,b,alpha,num_iter)\n", + " print(\"w:\", w)\n", + " print(\"b:\", b)\n", + " # 计算损失\n", + " loss = compute_loss(w,b)\n", + " print(\"loss:\", loss)\n", + "\n", + " plt.figure(dpi=600)\n", + " #plt.switch_backend('Agg') # 使用 Agg 渲染器\n", + " # 绘制数据点\n", + " plt.scatter(x, y, color='blue', label='original data')\n", + "\n", + " # 绘制回归直线\n", + " plt.plot(x, w*x + b, color='red', label='regression line')\n", + "\n", + " # 添加标题和标签\n", + " plt.title('linear regression')\n", + " plt.xlabel('x')\n", + " plt.ylabel('y')\n", + "\n", + " # 显示图例\n", + " plt.legend()\n", + "\n", + " # 显示图像\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pt", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/linear regression/1.py b/linear regression/1.py deleted file mode 100644 index bb8bf92..0000000 --- a/linear regression/1.py +++ /dev/null @@ -1,310 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -import torch.optim as optim -from torch.utils.data import DataLoader, TensorDataset -from matplotlib import pyplot as plt - - -def run1(): - def compute_error_for_line_given_points(b, w, points): - totalError = 0 - N = float(len(points)) - for i in range(len(points)): - x = points[i][0] - y = points[i][1] - totalError += (y - (w * x + b)) ** 2 - return totalError / N - - def step_gradient(b_current, w_current, points, learningRate): - b_gradient = torch.tensor(0.0, device=points.device, dtype=torch.float32) - w_gradient = torch.tensor(0.0, device=points.device, dtype=torch.float32) - N = float(len(points)) - for i in range(len(points)): - x = points[i][0] - y = points[i][1] - b_gradient += -(2 / N) * (y - (w_current * x + b_current)) - w_gradient += -(2 / N) * x * (y - (w_current * x + b_current)) - new_b = b_current - (learningRate * b_gradient) - new_w = w_current - (learningRate * w_gradient) - return [new_b, new_w] - - def gradient_descent_runner(points, starting_b, starting_w, learningRate, num_iterations): - b = torch.tensor(starting_b, device=points.device, dtype=torch.float32) - w = torch.tensor(starting_w, device=points.device, dtype=torch.float32) - for i in range(num_iterations): - b, w = step_gradient(b, w, points, learningRate) - return [b, w] - - def run(): - # 修改为生成数据的文件路径 - points_np = np.genfromtxt("data1.csv", delimiter=',').astype(np.float32) - points = torch.tensor(points_np, device='mps') - learning_rate = 0.0001 # 使用较小的学习率 - initial_b = 0.0 - initial_w = 0.0 - num_iterations = 1000 - print("Starting gradient descent at b={0},w={1},error={2}".format(initial_b, initial_w, - compute_error_for_line_given_points(initial_b, - initial_w, - points))) - print("running...") - [b, w] = gradient_descent_runner(points, initial_b, initial_w, learning_rate, num_iterations) - print("After gradient descent at b={0},w={1},error={2}".format(b.item(), w.item(), - compute_error_for_line_given_points(b, w, - points))) - - run() - -def run1_cuda(): - def compute_error_for_line_given_points(b, w, points): - totalError = 0 - N = float(len(points)) - for i in range(len(points)): - x = points[i][0] - y = points[i][1] - totalError += (y - (w * x + b)) ** 2 - return totalError / N - - def step_gradient(b_current, w_current, points, learningRate): - b_gradient = torch.tensor(0.0, device=points.device) - w_gradient = torch.tensor(0.0, device=points.device) - N = float(len(points)) - for i in range(len(points)): - x = points[i][0] - y = points[i][1] - b_gradient += -(2 / N) * (y - (w_current * x + b_current)) - w_gradient += -(2 / N) * x * (y - (w_current * x + b_current)) - new_b = b_current - (learningRate * b_gradient) - new_w = w_current - (learningRate * w_gradient) - return [new_b, new_w] - - def gradient_descent_runner(points, starting_b, starting_w, learningRate, num_iterations): - b = torch.tensor(starting_b, device=points.device) - w = torch.tensor(starting_w, device=points.device) - for i in range(num_iterations): - b, w = step_gradient(b, w, points, learningRate) - print("round:", i) - return [b, w] - - def run(): - points_np = np.genfromtxt("data1.csv", delimiter=',').astype(np.float32) - points = torch.tensor(points_np, device='cuda') - learning_rate = 0.0001 - initial_b = 0.0 - initial_w = 0.0 - num_iterations = 100000 - [b, w] = gradient_descent_runner(points, initial_b, initial_w, learning_rate, num_iterations) - print("After gradient descent at b={0}, w={1}, error={2}".format(b.item(), w.item(), - compute_error_for_line_given_points(b, w, - points))) - return b.item(), w.item() - - # 运行线性回归 - final_b, final_w = run() - - # 绘制图像 - points_np = np.genfromtxt("data1.csv", delimiter=',').astype(np.float32) - x = points_np[:, 0] - y = points_np[:, 1] - - x_range = np.linspace(min(x), max(x), 100) - y_pred = final_w * x_range + final_b - - plt.figure(figsize=(8, 6)) - plt.scatter(x, y, color='blue', label='Original data') - plt.plot(x_range, y_pred, color='red', label='Fitted line') - plt.xlabel('X') - plt.ylabel('Y') - plt.title('Fitting a line to random data') - plt.legend() - plt.grid(True) - plt.savefig('print1.png') - plt.show() - -def run1x(): - # 线性回归训练代码 - def compute_error_for_line_given_points(b, w, points): - totalError = 0 - N = float(len(points)) - for i in range(len(points)): - x = points[i][0] - y = points[i][1] - totalError += (y - (w * x + b)) ** 2 - return totalError / N - - def step_gradient(b_current, w_current, points, learningRate): - b_gradient = torch.tensor(0.0, device=points.device, dtype=torch.float32) - w_gradient = torch.tensor(0.0, device=points.device, dtype=torch.float32) - N = float(len(points)) - for i in range(len(points)): - x = points[i][0] - y = points[i][1] - b_gradient += -(2 / N) * (y - (w_current * x + b_current)) - w_gradient += -(2 / N) * x * (y - (w_current * x + b_current)) - new_b = b_current - (learningRate * b_gradient) - new_w = w_current - (learningRate * w_gradient) - return [new_b, new_w] - - def gradient_descent_runner(points, starting_b, starting_w, learningRate, num_iterations): - b = torch.tensor(starting_b, device=points.device, dtype=torch.float32) - w = torch.tensor(starting_w, device=points.device, dtype=torch.float32) - for i in range(num_iterations): - b, w = step_gradient(b, w, points, learningRate) - return [b, w] - - def run(): - points_np = np.genfromtxt("data1.csv", delimiter=',').astype(np.float32) - points = torch.tensor(points_np, device='mps') - learning_rate = 0.0001 - initial_b = 0.0 - initial_w = 0.0 - num_iterations = 5000 - [b, w] = gradient_descent_runner(points, initial_b, initial_w, learning_rate, num_iterations) - print("After gradient descent at b={0},w={1},error={2}".format(b.item(), w.item(), - compute_error_for_line_given_points(b, w, - points))) - return b.item(), w.item() - - # 运行线性回归 - final_b, final_w = run() - - # 绘制图像 - points_np = np.genfromtxt("data1.csv", delimiter=',').astype(np.float32) - x = points_np[:, 0] - y = points_np[:, 1] - - x_range = np.linspace(min(x), max(x), 100) - y_pred = final_w * x_range + final_b - - plt.figure(figsize=(8, 6)) - plt.scatter(x, y, color='blue', label='Original data') - plt.plot(x_range, y_pred, color='red', label='Fitted line') - plt.xlabel('X') - plt.ylabel('Y') - plt.title('Fitting a line to random data') - plt.legend() - plt.grid(True) - plt.savefig('print1.png') - plt.show() - -def run_m1(): - # 检查是否支持MPS（Apple Metal Performance Shaders） - device = torch.device("mps" if torch.backends.mps.is_available() else "cpu") - print(f"使用设备: {device}") - - # 生成示例数据 - # y = 3x + 2 + 噪声 - torch.manual_seed(0) - X = torch.linspace(-10, 10, steps=100).reshape(-1, 1) - y = 3 * X + 2 + torch.randn(X.size()) * 2 - - # 创建数据集和数据加载器 - dataset = TensorDataset(X, y) - dataloader = DataLoader(dataset, batch_size=10, shuffle=True) - - # 定义线性回归模型 - class LinearRegressionModel(nn.Module): - def __init__(self): - super(LinearRegressionModel, self).__init__() - self.linear = nn.Linear(1, 1) # 输入和输出都是1维 - - def forward(self, x): - return self.linear(x) - - # 实例化模型并移动到设备 - model = LinearRegressionModel().to(device) - - # 定义损失函数和优化器 - criterion = nn.MSELoss() - optimizer = optim.SGD(model.parameters(), lr=0.01) - - # 训练模型 - num_epochs = 100 - for epoch in range(num_epochs): - for batch_X, batch_y in dataloader: - batch_X = batch_X.to(device) - batch_y = batch_y.to(device) - - # 前向传播 - outputs = model(batch_X) - loss = criterion(outputs, batch_y) - - # 反向传播和优化 - optimizer.zero_grad() - loss.backward() - optimizer.step() - - if (epoch + 1) % 10 == 0: - print(f"Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}") - - # 保存整个模型 - torch.save(model.state_dict(), 'm1.pth') - print("整个模型已保存为 m1.pth") - - # 评估模型 - model.eval() - with torch.no_grad(): - X_test = torch.linspace(-10, 10, steps=100).reshape(-1, 1).to(device) - y_pred = model(X_test).cpu() - - plt.scatter(X.numpy(), y.numpy(), label='真实数据') - plt.plot(X_test.cpu().numpy(), y_pred.numpy(), color='red', label='预测线') - plt.legend() - plt.xlabel('X') - plt.ylabel('y') - plt.title('线性回归结果') - plt.show() - -def run_m1_test(): - # 定义线性回归模型结构 - class LinearRegressionModel(nn.Module): - def __init__(self): - super(LinearRegressionModel, self).__init__() - self.linear = nn.Linear(1, 1) # 输入和输出都是1维 - - def forward(self, x): - return self.linear(x) - - def main(): - # 检查是否支持MPS（Apple Metal Performance Shaders） - device = torch.device("mps" if torch.backends.mps.is_available() else "cpu") - print(f"使用设备: {device}") - - # 实例化模型并加载保存的模型参数 - model = LinearRegressionModel().to(device) - model.load_state_dict(torch.load('m1.pth')) - with open('m1.pth', 'rb') as f: - f.seek(0, 2) - size = f.tell() - print(f"模型文件大小: {size} 字节") - model.eval() - # 输出模型大小 - model_size = sum(p.numel() for p in model.parameters()) - print(f"模型大小: {model_size} 个参数") - print("模型参数已加载") - - # 生成测试数据 - X_test = torch.linspace(-10, 10, steps=100).reshape(-1, 1).to(device) - - # 使用加载的模型进行预测 - with torch.no_grad(): - y_pred = model(X_test).cpu() - - # 将测试数据移至CPU并转换为NumPy数组 - X_test_numpy = X_test.cpu().numpy() - y_pred_numpy = y_pred.numpy() - - # 可视化预测结果 - plt.scatter(X_test_numpy, 3 * X_test_numpy + 2, label='真实线性关系', color='blue') - plt.plot(X_test_numpy, y_pred_numpy, color='red', label='模型预测线') - plt.legend() - plt.xlabel('X') - plt.ylabel('y') - plt.title('加载模型后的线性回归预测结果') - plt.show() - - main() - -if __name__ == '__main__': - print("start") diff --git a/linear regression/data1.csv b/linear regression/data1.csv deleted file mode 100644 index 508fd99..0000000 --- a/linear regression/data1.csv +++ /dev/null @@ -1,100 +0,0 @@ -0.0,3.267264598063918 -0.10101010101010101,3.3715980311448757 -0.20202020202020202,3.624529195538264 -0.30303030303030304,2.2426026435865785 -0.40404040404040403,2.881354128303834 -0.5050505050505051,4.108915299601898 -0.6060606060606061,3.2833072841616344 -0.7070707070707071,3.401314666490608 -0.8080808080808081,3.4471224977820083 -0.9090909090909091,4.597483332850038 -1.0101010101010102,4.1948230194917615 -1.1111111111111112,4.770110614428998 -1.2121212121212122,4.3466984672473545 -1.3131313131313131,4.085374736788284 -1.4141414141414141,4.860667770156386 -1.5151515151515151,5.367460741298345 -1.6161616161616161,5.1076464505556585 -1.7171717171717171,4.517380143483942 -1.8181818181818181,6.028333717306668 -1.9191919191919191,5.268642728341781 -2.0202020202020203,5.2032646463511885 -2.121212121212121,5.776924577040542 -2.2222222222222223,5.914239664440679 -2.323232323232323,6.195294604152318 -2.4242424242424243,6.67461745554651 -2.525252525252525,6.62895898059055 -2.6262626262626263,6.423496434474387 -2.727272727272727,6.520626001853953 -2.8282828282828283,6.252851138402289 -2.929292929292929,7.045662416151556 -3.0303030303030303,7.062687254815803 -3.131313131313131,6.950015155958233 -3.2323232323232323,7.71420449451215 -3.3333333333333335,7.536987534120887 -3.4343434343434343,8.408446293914908 -3.5353535353535355,8.281116903817127 -3.6363636363636362,6.862064335470844 -3.7373737373737375,8.455114086555362 -3.8383838383838382,8.610569256326439 -3.9393939393939394,8.695172603505283 -4.040404040404041,7.987174933011048 -4.141414141414141,8.484042583282307 -4.242424242424242,8.152218590549857 -4.343434343434343,8.810112829362456 -4.444444444444445,9.098520210970904 -4.545454545454545,9.315991463976044 -4.646464646464646,9.266562387635002 -4.747474747474747,8.457655714255173 -4.848484848484849,8.577190426286784 -4.94949494949495,9.992687218959654 -5.05050505050505,9.949888900251127 -5.151515151515151,10.112370557219064 -5.252525252525253,10.250084050804231 -5.353535353535354,9.675169646286898 -5.454545454545454,9.790565255890696 -5.555555555555555,9.91666488079517 -5.656565656565657,10.325538746448835 -5.757575757575758,9.77548528051785 -5.858585858585858,10.55371401462777 -5.959595959595959,10.757696722894282 -6.0606060606060606,10.893354131765726 -6.161616161616162,12.049342074375708 -6.262626262626262,10.936118426966079 -6.363636363636363,11.031578580287063 -6.4646464646464645,11.713471927909302 -6.565656565656566,12.343664117608101 -6.666666666666667,12.067856620638729 -6.767676767676767,11.814430199711552 -6.8686868686868685,11.123516182999314 -6.96969696969697,12.496962644202316 -7.070707070707071,12.767487737755147 -7.171717171717171,12.632934104476211 -7.2727272727272725,12.728225932468364 -7.373737373737374,12.97630338885533 -7.474747474747475,12.896220727223701 -7.575757575757575,13.047808359849581 -7.6767676767676765,13.110443597152527 -7.777777777777778,12.921358752181128 -7.878787878787879,13.30038615173782 -7.979797979797979,13.836945395153705 -8.080808080808081,13.054484897082014 -8.181818181818182,14.01038452336861 -8.282828282828282,13.643336312636018 -8.383838383838384,14.564671365817466 -8.484848484848484,14.040540515755758 -8.585858585858587,14.57992522742261 -8.686868686868687,14.88631275019171 -8.787878787878787,14.021963220008606 -8.88888888888889,15.068155050128949 -8.98989898989899,15.083538874549268 -9.09090909090909,15.417748308319911 -9.191919191919192,14.89714205401168 -9.292929292929292,14.534676091762206 -9.393939393939394,15.556467883324295 -9.494949494949495,15.525938847099864 -9.595959595959595,15.560767751324764 -9.696969696969697,15.982790914773943 -9.797979797979798,16.062079721169738 -9.8989898989899,16.232818049890696 -10.0,17.053472736980353 diff --git a/linear regression/liner regression.ipynb b/linear regression/liner regression.ipynb deleted file mode 100644 index bc0cf52..0000000 --- a/linear regression/liner regression.ipynb +++ /dev/null @@ -1,5234 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4", - "mount_file_id": "1kCjTIM3v5mm8NXbKPLmlRKl7twd9xU94", - "authorship_tag": "ABX9TyOLIwV8MmfP7m3yiBMZyYF+" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "!pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121\n", - "!pip install numpy pandas pillow matplotlib" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "d5k_A4uC6e3f", - "executionInfo": { - "status": "ok", - "timestamp": 1718270822835, - "user_tz": -480, - "elapsed": 94497, - "user": { - "displayName": "wolves li", - "userId": "01742783151929573764" - } - }, - "outputId": "15326d01-d0fe-400f-bdc9-915e4ab959b8" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Looking in indexes: https://download.pytorch.org/whl/cu121\n", - "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.0+cu121)\n", - "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.18.0+cu121)\n", - "Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (2.3.0+cu121)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.14.0)\n", - "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n", - "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12.1)\n", - "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n", - "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n", - "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n", - 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"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.1)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# cuda\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import torch\n", - "\n", - "# 线性回归训练代码\n", - "def compute_error_for_line_given_points(b, w, points):\n", - " totalError = 0\n", - " N = float(len(points))\n", - " for i in range(len(points)):\n", - " x = points[i][0]\n", - " y = points[i][1]\n", - " totalError += (y - (w * x + b)) ** 2\n", - " return totalError / N\n", - "\n", - "def step_gradient(b_current, w_current, points, learningRate):\n", - " b_gradient = torch.tensor(0.0, device=points.device)\n", - " w_gradient = torch.tensor(0.0, device=points.device)\n", - " N = float(len(points))\n", - " for i in range(len(points)):\n", - " x = points[i][0]\n", - " y = points[i][1]\n", - " b_gradient += -(2 / N) * (y - (w_current * x + b_current))\n", - " w_gradient += -(2 / N) * x * (y - (w_current * x + b_current))\n", - " new_b = b_current - (learningRate * b_gradient)\n", - " new_w = w_current - (learningRate * w_gradient)\n", - " return [new_b, new_w]\n", - "\n", - "def gradient_descent_runner(points, starting_b, starting_w, learningRate, num_iterations):\n", - " b = torch.tensor(starting_b, device=points.device)\n", - " w = torch.tensor(starting_w, device=points.device)\n", - " for i in range(num_iterations):\n", - " print(\"round:\" + str(i))\n", - " b, w = step_gradient(b, w, points, learningRate)\n", - " return [b, w]\n", - "\n", - "def run():\n", - " points_np = np.genfromtxt(\"/content/drive/MyDrive/PT/data1.csv\", delimiter=',').astype(np.float64)\n", - " points = torch.tensor(points_np, device='cuda')\n", - " learning_rate = 0.0001\n", - " initial_b = 0.0\n", - " initial_w = 0.0\n", - " num_iterations = 100000\n", - " [b, w] = gradient_descent_runner(points, initial_b, initial_w, learning_rate, num_iterations)\n", - " print(\"After gradient descent at b={0}, w={1}, error={2}\".format(b.item(), w.item(),\n", - " compute_error_for_line_given_points(b, w, points)))\n", - " return b.item(), w.item()\n", - "\n", - "# 运行线性回归\n", - "final_b, final_w = run()\n", - "\n", - "# 绘制图像\n", - "points_np = np.genfromtxt(\"/content/drive/MyDrive/PT/data1.csv\", delimiter=',').astype(np.float64)\n", - "x = points_np[:, 0]\n", - "y = points_np[:, 1]\n", - "\n", - "x_range = np.linspace(min(x), max(x), 100)\n", - "y_pred = final_w * x_range + final_b\n", - "\n", - "plt.figure(figsize=(8, 6))\n", - "plt.scatter(x, y, color='blue', label='Original data')\n", - "plt.plot(x_range, y_pred, color='red', label='Fitted line')\n", - "plt.xlabel('X')\n", - "plt.ylabel('Y')\n", - "plt.title('Fitting a line to random data')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.savefig('/content/drive/MyDrive/PT/print1.png')\n", - "plt.show()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "4uVNEBT96_HK", - "executionInfo": { - "status": "ok", - "timestamp": 1718272497978, - "user_tz": -480, - "elapsed": 1187345, - "user": { - "displayName": "wolves li", - "userId": "01742783151929573764" - } - }, - "outputId": "7f495cb6-2e31-4569-f0f2-38587cb48f89" - }, - "execution_count": 17, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001B[1;30;43m流式输出内容被截断，只能显示最后 5000 行内容。\u001B[0m\n", - "round:95001\n", - "round:95002\n", - "round:95003\n", - "round:95004\n", - "round:95005\n", - "round:95006\n", - "round:95007\n", - "round:95008\n", - "round:95009\n", - "round:95010\n", - "round:95011\n", - "round:95012\n", - "round:95013\n", - "round:95014\n", - "round:95015\n", - "round:95016\n", - 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"round:99893\n", - "round:99894\n", - "round:99895\n", - "round:99896\n", - "round:99897\n", - "round:99898\n", - "round:99899\n", - "round:99900\n", - "round:99901\n", - "round:99902\n", - "round:99903\n", - "round:99904\n", - "round:99905\n", - "round:99906\n", - "round:99907\n", - "round:99908\n", - "round:99909\n", - "round:99910\n", - "round:99911\n", - "round:99912\n", - "round:99913\n", - "round:99914\n", - "round:99915\n", - "round:99916\n", - "round:99917\n", - "round:99918\n", - "round:99919\n", - "round:99920\n", - "round:99921\n", - "round:99922\n", - "round:99923\n", - "round:99924\n", - "round:99925\n", - "round:99926\n", - "round:99927\n", - "round:99928\n", - "round:99929\n", - "round:99930\n", - "round:99931\n", - "round:99932\n", - "round:99933\n", - "round:99934\n", - "round:99935\n", - "round:99936\n", - "round:99937\n", - "round:99938\n", - "round:99939\n", - "round:99940\n", - "round:99941\n", - "round:99942\n", - "round:99943\n", - "round:99944\n", - "round:99945\n", - "round:99946\n", - "round:99947\n", - "round:99948\n", - "round:99949\n", - "round:99950\n", - "round:99951\n", - "round:99952\n", - "round:99953\n", - "round:99954\n", - "round:99955\n", - "round:99956\n", - "round:99957\n", - "round:99958\n", - "round:99959\n", - "round:99960\n", - "round:99961\n", - "round:99962\n", - "round:99963\n", - "round:99964\n", - "round:99965\n", - "round:99966\n", - "round:99967\n", - "round:99968\n", - "round:99969\n", - "round:99970\n", - "round:99971\n", - "round:99972\n", - "round:99973\n", - "round:99974\n", - "round:99975\n", - "round:99976\n", - "round:99977\n", - "round:99978\n", - "round:99979\n", - "round:99980\n", - "round:99981\n", - "round:99982\n", - "round:99983\n", - "round:99984\n", - "round:99985\n", - "round:99986\n", - "round:99987\n", - "round:99988\n", - "round:99989\n", - "round:99990\n", - "round:99991\n", - "round:99992\n", - "round:99993\n", - "round:99994\n", - "round:99995\n", - "round:99996\n", - "round:99997\n", - "round:99998\n", - "round:99999\n", - "After gradient descent at b=2.8940329551696777, w=1.339672565460205, error=0.1857876382824373\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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999+Nhx56yKhcubIREBBgREREGHfeeaexZMmSHL+XrH6+hmEY3377rdG8eXMjJCTECA0NNTp16mTs2rUrx/e0f48ffPBBpucuXLhgPPnkk0ZYWJgREhJiNG/e3Fi7dq3RunVrh7ZWWb2Hs38/hmEYM2bMMKpWrWoEBQUZjRo1MlatWpXpPQ3DMI4ePWr06dPHKF++vBEYGGjUrVs303tl9fPPakw5/ZtP78MPPzRq1qxpBAUFGbVq1TISExMz3X/DMIx58+YZ1atXN4KCgowaNWoY8+fPd/pv/tdffzVatWplhISEGEDav/NTp06lfZ8lS5Y0OnToYPz6669OfxdEJGsWwyiACnsRkVzq0qULO3fudFqXKCIiVx7VvIpIoZFxK9c9e/bwxRdfEBMT450BiYhIoaOZVxEpNMLCwujduzfVqlXjzz//ZObMmaSkpLB582anvThFROTKowVbIlJo3HbbbSxevJgjR44QFBRE06ZNmThxooKriIik0cyriIiIiPgM1byKiIiIiM9QeBURERERn1Hka15tNhuHDh2iVKlS+bKtpYiIiIjkjWEY/Pvvv4SHhztsFe1MkQ+vhw4dIioqytvDEBEREZEcHDhwgMjIyGzPKfLh1b614IEDBwgNDc3366WmpvLNN99w6623EhAQkO/XE8/TPfR9uoe+TffP9+ke+r6CvofJyclERUW5tCV0kQ+v9lKB0NDQAguvxYsXJzQ0VL+wPkr30PfpHvo23T/fp3vo+7x1D10p8dSCLRERERHxGQqvIiIiIuIzFF5FRERExGcU+ZpXVxiGwaVLl7BarXl+r9TUVIoVK8aFCxc88n5S8LK6hwEBAfj7+3txZCIiInLFh9eLFy9y+PBhzp0755H3MwyDypUrc+DAAfWV9VFZ3UOLxUJkZCQlS5b04uhERESubFd0eLXZbOzbtw9/f3/Cw8MJDAzMc+C02WycOXOGkiVL5thkVwonZ/fQMAz+/vtvDh48SPXq1TUDKyIi4iVXdHi9ePEiNpuNqKgoihcv7pH3tNlsXLx4keDgYIVXH5XVPaxQoQL79+8nNTVV4VVERMRLlK5AIVNcojIQERER71NqExERERGfofAqIiIiIj5D4fUKtH//fiwWC1u2bHH5NQkJCVx11VVeHwdAdHQ08fHxHh2LiIiI+AaFVx914MAB+vbtm9Yl4eqrr2bIkCGcOHEix9dGRUVx+PBh6tSp4/L17rvvPn777be8DNlr8iN4i4iIiHcovHqA1QorV8LixebX/N6b4I8//qBRo0bs2bOHxYsXs3fvXmbNmsXy5ctp2rQpJ0+ezPK1Fy9exN/fn8qVK1OsmOvNJkJCQqhYsaInhi8iIiKSawqveZSYCNHR0KYN9OgBbdv6Ua9eKImJ+XfNgQMHEhgYyDfffEPr1q2pUqUKHTt25NtvvyUpKYnnn38+7dzo6GjGjRvHQw89RGhoKI8++qjTj+s/+eQTqlevTnBwMG3atGHBggVYLBb++ecfIPPs5ejRo2nQoAHvvPMO0dHRlC5dmvvvv59///037ZyvvvqKFi1acNVVV1GuXDnuvPNOfv/9d7e+12PHjtGpUydCQkKoWrUqCxcuzHTOa6+9Rt26dSlRogRRUVEMGDCAM2fOALBy5Ur69OnD6dOnsVgsWCwWRo8eDcA777xDo0aNKFWqFJUrV6ZHjx4cO3bMrfGJiIgUNVYrrFlj/n3NmvyflHOXwmseJCZC165w8KDj8cOHLXTrZsmXAHvy5Em+/vprBgwYQEhIiMNzlStXpmfPnrz33nsYhpF2/NVXX6V+/fps3ryZUaNGZXrPffv20bVrV7p06cLWrVvp37+/QwDOyu+//87SpUv57LPP+Oyzz/j+++956aWX0p4/e/Ysw4YNY8OGDSxfvhw/Pz/uvvtubDaby99v7969OXDgACtWrGDJkiXMmDEjU8D08/PjjTfeYOfOnSxYsIDvvvuOp59+GoBmzZoRHx9PaGgohw8f5vDhwwwfPhwwt4EdN24cW7duZenSpezfv5/evXu7PDYREZGixj4pd8cd5uM77jAf5+eknLuu6E0K8sJqhSFDIF1GTGMYFiwWg7g46NwZPNnPfs+ePRiGQc2aNZ0+X7NmTU6dOsXff/+d9jH/LbfcwpNPPpl2zv79+x1eM3v2bK6//npeeeUVAK6//np27NjBhAkTsh2LzWYjISGBUqVKAfDggw+yfPnytNfdc889Due//fbbVKhQgV27drlUb/vbb7/x5Zdf8vPPP9O4cWMA5s2bl+l7j4uLS/t7dHQ048eP57HHHmPGjBkEBgZSunRpLBYLlStXdnhd37590/5erVo13njjDRo3bpw2aysiInIlsU/KGQaknx9LSjKPL1kCsbHeG5+dZl5zafXqzDOu6RmGhQMHzPPyg+EsNWehUaNG2T6/e/futHBo16RJkxzfNzo6Oi24AoSFhTnMiu7Zs4fu3btTrVo1QkNDiY6OBuCvv/5yady//PILxYoV48Ybb0w7VqNGjUyLr7799lvatm1LREQEpUqV4sEHH+TEiROcO3cu2/ffuHEjnTp1okqVKpQqVYrWrVu7NT4REZGiIvtJOfNrXFzhKCFQeM2lw4c9e56rrr32WiwWC7/88ovT53/55RfKlClDhQoV0o6VKFHCs4P4T0BAgMNji8XiUBLQqVMnTp48ydy5c1m3bh3r1q0DzEVjnrJ//37uvPNO6tWrx4cffsjGjRuZPn16jtc5e/YsHTp0IDQ0lIULF7J+/Xo++ugjj49PRETEF+Q8KUe+Tsq5Q+E1l8LCPHueq8qVK0f79u2ZMWMG58+fd3juyJEjLFy4kPvuu8+trUyvv/56NmzY4HBs/fr1eRrniRMn2L17NyNHjqRt27Zp5QzuqFGjBpcuXWLjxo1px3bv3p22iAzM2VObzcaUKVO4+eabue666zh06JDD+wQGBmLN8J+Kv/76KydOnOCll16iZcuW1KhRQ4u1RETkiuWtSbncUHjNpZYtITISssqIFotBVJR5nqe9+eabpKSk0KFDB1atWsWBAwf46quvaN++PRERETnWqmbUv39/fv31V0aMGMFvv/3G+++/T0JCwn/fh+shOL0yZcpQrlw55syZw969e/nuu+8YNmyYW+9x/fXXc9ttt9G/f3/WrVvHxo0befjhhx0Wql177bWkpqYybdo0/vjjD9555x1mzZrl8D7R0dGcOXOG5cuXc/z4cc6dO0eVKlUIDAxMe90nn3zCuHHjcvW9ioiI+DpvTcrlhsJrLvn7w9Sp5t8z5juLxSwOiY/37GItu+rVq7NhwwaqVatGt27duOaaa3j00Udp06YNa9eupWzZsm69X9WqVVmyZAmJiYnUq1ePmTNnpnUbCAoKytUY/fz8ePfdd9m4cSN16tRh6NChaQvC3DF//nzCw8Np3bo1sbGxPProow79ZuvXr89rr73Gyy+/TJ06dVi4cCGTJk1yeI9mzZrx2GOPcd9991GhQgUmT55MhQoVSEhI4IMPPqBWrVq89NJLvPrqq7n6XkVERHxdzpNy5NuknLsshjsrf3xQcnIypUuX5vTp04SGhjo8d+HCBfbt20fVqlUJDg7O1fsnJpoFzunrRCIibMTHQ9euvvvfBhMmTGDWrFkcOHDA20MpcDabjeTkZEJDQ/Hzu3wPPfHvRQpGamoqX3zxBbfffnum2mwp/HT/fJ/uoW+ydxsACA5OZfHiL+je/XYuXDDvYX52G8gur2WkVll5FBtrtsNavdqsA6lUyUb9+smUKZP9D76wmTFjBo0bN6ZcuXL88MMPvPLKKzzxxBPeHpaIiIgUkNhYM6AOGQLpd5uPjDQ/TS4MbbJA4dUj/P0hJsb8u80GycleHU6u7Nmzh/Hjx3Py5EmqVKnCk08+ybPPPuvtYYmIiEgBsk/KrVpl5pnPP4dWrfKnDDK3FF4FgNdff53XX3/d28MQERERL/P3hxYt4IsvzK+FKbiCFmyJiIiIiA9ReBURERERn6HwKiIiIiI+Q+FVRERERHyGwquIiIiI+AyFVxERERHxGQqvRUxMTAxxcXEFdr2EhASuuuqqLJ/fv38/FouFLVu2ALBy5UosFgv//PNPgYxPREREihaFVx/Uu3dvLBZLpj979+4lMTGRcePGpZ0bHR1NfHy8w+tzCpz5qVmzZhw+fJjSpUt75foiIiLi27RJgY+67bbbmD9/vsOxChUq4F/YOglnEBgYSOXKlb09DBEREfFRmnnNyDDg7NmC/2MYbg0zKCiIypUrO/zx9/d3KBuIiYnhzz//ZOjQoWmzsytXrqRPnz6cPn067djo0aMBSElJYfjw4URERFCiRAluuukmVq5c6XDdhIQEqlSpQvHixbn77rs5kX7zYxdkLBuwzwJ//fXX1KxZk5IlS3Lbbbdx+PBhh9e99dZb1KxZk+DgYGrUqMGMGTPcuq6IiIgUDZp5zejcOShZMtcv9wOuys0Lz5yBEiVyfV1nEhMTqV+/Po8++iiPPPIIAGXLliU+Pp4XXniB3bt3A1Dyv+/3iSeeYNeuXbz77ruEh4fz0Ucfcdttt7F9+3aqV6/OunXr6NevH5MmTaJLly589dVXvPjii3ke57lz53j11Vd555138PPz44EHHmD48OEsXLgQgIULF/LCCy/w5ptv0rBhQzZv3swjjzxCiRIl6NWrV56vLyIiIr5D4dVHffbZZ2mhE6Bjx4588MEHDueULVsWf39/SpUq5fBRfenSpbFYLA7H/vrrL+bPn89ff/1FeHg4AMOHD+err75i/vz5TJw4kalTp3Lbbbfx9NNPA3Ddddfx448/8tVXX+Xpe0lNTWXWrFlcc801gBmix44dm/b8iy++yJQpU4iNjQWgatWq7Nq1i9mzZyu8ioiI5JLVCqtXw+HDEBYGLVtCIa8+BBReMyte3JwFzSWbzUZycjKhoaH4+blRlVG8uFvXadOmDTNnzkx7XCKPs7bbt2/HarVy3XXXORxPSUmhXLlyAPzyyy/cfffdDs83bdo0z+G1ePHiacEVICwsjGPHjgFw9uxZfv/9d/r165c2ewxw6dIlLfoSERHJpcREGDIEDh68fCwyEqZOhdhYzHJGq9Vr48uOwmtGFkvePr632cybXaIEuBNe3VSiRAmuvfZaj73fmTNn8Pf3Z+PGjZkWfZXMQxmFKwICAhweWywWjP9qgM/89x8Sc+fO5aabbnI4r7AvThMRESmMEhOha9fMy22Skszjn847RsdPH6Omnx906uSdQWZD4bWICwwMxJrhv5ycHWvYsCFWq5Vjx47RsmVLp+9Vs2ZN1q1b53Dsp59+8uyAM6hUqRLh4eH88ccf9OzZM1+vJSIiUtRZreaMq7N14oYBXVnCzQ8/jp/tONcEBmI7cgSiogp+oNlQeC3ioqOjWbVqFffffz9BQUGUL1+e6Ohozpw5w/Lly6lfvz7Fixfnuuuuo2fPnjz00ENMmTKFhg0b8vfff7N8+XLq1avHHXfcweDBg2nevDmvvvoqnTt35uuvv85zyYArxowZw+DBgyldujS33XYbKSkpbNiwgVOnTjFs2LB8v76IiEhRsXq1Y6mAXVlO8CZP0J13wQZnqtVl/RN9aFEI21uqVVYRN3bsWPbv388111xDhQoVAHOjgMcee4z77ruPChUqMHnyZADmz5/PQw89xJNPPsn1119Ply5dWL9+PVWqVAHg5ptvZu7cuUydOpX69evzzTffMHLkyHz/Hh5++GHeeust5s+fT926dWndujUJCQlUrVo1368tIiLi66xWWLkSFi+G5cszP9+JT9hJbbrzLpfwZxwj+Xz0WpKrVSvwsbrCYhhuNhj1McnJyZQuXZrTp08TGhrq8NyFCxfYt28fVatWJTg42CPXy/WCLSk0srqH+fHvRfJHamoqX3zxBbfffnummmop/HT/fJ/uYeHhbGGW3VWcYipDeIh3ANhJLXqTwAYa8913qSQnF9w9zC6vZaSyAREREZEiIn37qz17YPRo5/Wtt/Elb/EwERzCih+v8BSjGc1FSzBRkdC0KXz9dYEP3yVenRpctWoVnTp1Ijw8HIvFwtKlSzOd88svv3DXXXdRunRpSpQoQePGjfnrr78KfrAiIiIihVhiIkRHQ5s20KMHvPhi5uAaymnm8jBfcjsRHGI319GCNTzLS1y0mJ8qxscX7n6vXg2vZ8+epX79+kyfPt3p87///jstWrSgRo0arFy5km3btjFq1Ch9ZCsiIiKSjr39lbPyALu2fMt26vIw87Bh4TWG0pDN/ERTwOzzumTJf31eCzGvlg107NiRjh07Zvn8888/z+233562oAhwaGYvIiIicqXLrv0VQAnOMJmnGYC5udHvVKMP81lNK0aOhFq1tMOWR9hsNj7//HOefvppOnTowObNm6latSrPPvssXbp0yfJ1KSkppKSkpD1OTk4GzOLx1NRUh3MvXbqEYRhYrVZsNptHxm1f/2YYhsfeUwpWVvfQarViGAaXLl3K9G9JChf7/dF98k26f75P97BgrVkDJ05ASEjm51pYVzE79RGqGvsAmO3/GCMDJnLWUpIQUrnlFmjRwjzXZjP/QMHfQ3euU2i6DVgsFj766KO0YHrkyBHCwsIoXrw448ePp02bNnz11Vc899xzrFixgtatWzt9n9GjRzNmzJhMxxctWkTxDFuwWiwWwsLCqFy5MqVKlfL49yRFy7lz5zh06BCHDx/Wf5iIiEih5p+SQs133uGazz4D4FyFCmx+4gmO16/v5ZE5d+7cOXr06OFSt4FCG14PHTpEREQE3bt3Z9GiRWnn3XXXXZQoUYLFixc7fR9nM69RUVEcP37c6Q/j6NGjJCcnU6FCBYoXL47FYsnT92EYBmfPnqVEiRJ5fi/xDmf30GazcfjwYYoVK0ZERITubSGXmprKsmXLaN++vdr0+CDdP9+ne1iw1qyBO+64/Pgm61rmpvbjWmMvAG/79+PZgJf512LmIPv/hb3zTta7vxb0PUxOTqZ8+fK+3SqrfPnyFCtWjFq1ajkcr1mzJmvWrMnydUFBQQQFBWU6HhAQ4PSHHxERgb+/P8ePH8/7oDGDz/nz5wkJCVHA8VFZ3UM/Pz8iIiIIDAz04ujEHVn93otv0P3zfbqHBaNVKyhXDo4fvMBYRvEkU/DD4CAR9GMe31g7QLpd4aOizI4CrizMKqh76M41Cm14DQwMpHHjxuzevdvh+G+//cbVV1/tsevYSwcqVqzokbqO1NRUVq1aRatWrfQL66OyuoeBgYHaeEJERAodf3/4vyd+pvIzvajJrwAk0Is44km2XAUGjBkD1av71sKsrHg1vJ45c4a9e/emPd63bx9btmyhbNmyVKlShaeeeor77ruPVq1apdW8fvrpp6xcudLjY/H398ffA3fS39+fS5cuERwcrPDqo3QPRUTEZ6SkwNixtHnpJcDGUb/KPGybw2eY9QBRka7PsvoKr4bXDRs20KZNm7THw4YNA6BXr14kJCRw9913M2vWLCZNmsTgwYO5/vrr+fDDD2lhXxYnIiIicqXavBl69YLt283HPXpQPn4aT+4sS4/DRWOW1RmvhteYmBhyWi/Wt29f+vbtW0AjEhERESnkUlNhwgTzz6VLUKECzJoFsbH4AzEx3h5g/iq0Na8iIiIiVxKrFVavhsPZzZpu327Otm7eDMBfTbpy4NkZ3NypAkVsgjVLCq8iIiIiXpaYaO6SlX5718hIeO01c2L1yMFLNFk5mar/NxpLaiqn/MrymG0G7/98H9xtnjt1atGqbc2KwquIiIiIFyUmQteumbd3PXgQunWDGvzCAnpRjfUAfMxd9LfN5iiV085NSjLfY8mSoh9g1fdHRERExEusVnPG1dkSID+sPMmrbKYhTVjPKa7iQf6PLix1CK5w+fVxceZ7FmWaeRURERHJI5fqVZ1YvdqxVMDuWvaQQG+a8yMAX9CRR5jLISKyfC/DgAMHzPcsyou2NPMqIiIikgeJiRAdDW3aQI8e5tfoaPN4Tg4fdnxswcZgprKV+jTnR5IpRT/e4g4+zza4ZveeRY3Cq4iIiEgu2etVM86e2mtQcwqwYWGX/16VP/iOW5hKHMU5zzLaUYcdvE0/wPUt59O/Z1Gk8CoiIiKSC9nVq7pag9qyJURF2HicmWyjHjF8zxlK8BgzuZVvOEAVl8djsUBUlPmeRZnCq4iIiEguZFWvape+BjUr/gf/ZEPZW5nBAEpylpW0ph7bmM1juDPbavnv1Pj4orejVkYKryIiIiK54GptqdPzDAPeegvq1qXi9uVcCgzhhdJTuYXv2Ee1LN/LHlLLlXM8Hhl5ZbTJAnUbEBEREckVV2tLM5138CA88gh89ZX5uFkzis2fz4vXXMct6ToWHD8OQ4dm3rggPh46d85dd4OiQOFVREREJBdatjTDZFKS87pXi8V8Pq0G1TDgnXdg8GA4fRqCgmD8eDOh+vvjT+YWV3ffnXVILcrtsLKj8CoiIiKSC/7+5pasXbuaQTV9gM1Ug3rkCPTvD598Yj7RuDEsWAA1a+Z4jSs1pGZFNa8iIiIiuRQba9aaRmRowZpWg3q3Ae++C7Vrm8E1IAAmTIAff8wxuIpzmnkVERERySCrHbOcHY+NzaIG9eTf0G2AmWIBGjY0Z1vr1vXuN+fjFF5FRERE0klMNPu3Zlwo1b07LF6c+fjUqWaAdfh4PzERHnsM/v4bihWDkSPhuefMmVfJE4VXERERkf/Yd8zKuADr4EF45ZXM59t30kprU3XiBAwaZKZcMGdZExLghhvye+hXDNW8ioiIiJD9jllZcdhJa+knZm3r4sVmjcHzz8P69QquHqaZVxERERFy3jErK6HGP4w9EIf/3QvMAzVrmrWtjRt7doACKLyKiIjIFSKrRVh2ru6YlV4HvuItHiaSJAyLBcvw4TB2LAQH53g9yR2FVxERESnyslqEZV9sBa7vmAVQimSm8CSP8BYAv1GdM28kcMMTzVy+nuSOal5FRESkSLMvwspYEmBfbJWYaD6275hl32AgK7ewnO3UTQuuUxnCnRFbqP/45eDqyvUkdxReRUREpMjKbhGWw2Ir6+Uds8B5gC3BGaYzgOW042r+4g+qEsNKhlrieemN4ml9YF29nuSOwquIiIgUWTktwjIMOHDAPA+y3jHrngqr+LN0fQYwE4AZPE49tvFHVOvLbbJycT1xn2peRUREpMhydRFW+vPS75h1bP85mn32HBGJb2AxDIwqVdg2eB5lwtvxWR4WfeVmcZiYFF5FRESkyHJ1EVbG8/z9ISZoLUzsBXv2mAcffhjLlCnUDw2lvoevJ65T2YCIiIgUWTktwrJYICrKPC/NhQvw9NPQooUZXCMi4MsvYe5cCA31/PXELQqvIiIiUmRltwjL/jg+Pt1H//YdsV55BWw2eOgh2LEDbrstf64nblN4FRERkULPaoWVK82dV1euzH61fsZzO3d2vggrMpLLi61SUmDkSGjaFH75BSpVgo8/NnfKuuoqt8aa1aIvh+tJrqnmVURERAo1dxr+Z3fu/v1Z7Hi1ZYs5w7p9u/mC7t1h2jQoVy7XY06/6Cvj9bTzVt4ovIqIiEihZW/4n7Fvqr3hf/qZTHfOBSA1FcZPhPHj4dIlKF8eZs2Ce+7xyNj9/SEmJvP3o5238kZlAyIiIlIoudPw3+3NAbZvh5tugtGjzeB6zz2wc6fHgqsz2nnLMxReRUREpFByp+G/q+euWXkJJk2CG2+EzZuhbFmzOPaDD6BiRc9/E//Rzlueo7IBERERKZQ83fC/Br9Qu39v+P1n80CnTjBnDlSunKvxucOdIJ6x1EAcaeZVRERECiV3Gv5nd64fVp7kVTbTkPK//wylS5tdBD7+uECCK2jnLU/SzKuIiIgUSvaG/0lJzj9ut1jM5+0N/52dey17SKA3zfkRAKPDbVjemmueXIC085bnaOZVRERECiV3Gv5nPNeCjUG8wVbq05wfSaYUGx9/C8uXXxR4cAXtvOVJCq8iIiJSaLnT8N9+7s2V9rGctrzBEIpznjVBbflh5nZunNEv6/SYz7TzlueobEBEREQKtewa/jswDGKPzebuf4dj4SyXgorzx2Ov0PSVx/AP8P58nT1cO+vzGh+vPq+uUngVERGRQs9Zw38Hf/0F/frBt99iAWjVimLz53NdtWoFM0AXuRzEJUsKryIiIuK7DAPmz4ehQyE5GUJCzD6ugwaBn/dnW53JMYhLthReRURExDclJcGjj8IXX5iPmzaFhAS47jqvDkvyV+H8TxIRERGRrBgGvPMO1KljBtegIJg82fwsXsG1yNPMq4iIiPiOI0fgscfMDQaA5OsbsbrfAko0rkVLQKWjRZ/Cq4iIiBQ4q9XNRUuGAe+9BwMHwsmT2IoF8Erx0Ty/+2msT5txJjLSbEfl7qp9t8ciXuXVsoFVq1bRqVMnwsPDsVgsLF26NMtzH3vsMSwWC/Hx8QU2PhEREfG8xESIjoY2baBHD/NrdLR53Km//4Zu3aB7dzh5kn+qNqDhpQ08k/wc1nTzcElJ0LVrNu/jibGI13k1vJ49e5b69eszffr0bM/76KOP+OmnnwgPDy+gkYmIiEh+SEw0A2b6PqeQTfBMTITatc0GqcWKYRv1Ig0v/sw26mV6b/u2sHFx5myqx8cihYJXw2vHjh0ZP348d999d5bnJCUlMWjQIBYuXEhAQEABjk5EREQ8yWo1G/TbQ2Z6mYLnyZPQsyfcc48581qnDqxbx6pbRrM/Kes8YBhw4IBZBuCxsUihUqhrXm02Gw8++CBPPfUUtWvXduk1KSkppKSkpD1OTk4GIDU1ldTU1HwZZ3r2axTEtSR/6B76Pt1D36b75/uyuodr1sCJE2Yr1qwcPw47X/6cutMex3LkCIafH7annsI2ciQEBXF4SWq2r7c7fBiy+yfk6lhWrYIWLXK+XlFT0L+H7lzHYhjO/puj4FksFj766CO6dOmSdmzSpEmsWLGCr7/+GovFQnR0NHFxccTFxWX5PqNHj2bMmDGZji9atIjixYvnw8hFRETEE4qdOUPdefOosmIFAP9GRrJp8GD+UfurIu/cuXP06NGD06dPExoamu25hXbmdePGjUydOpVNmzZhsVhcft2zzz7LsGHD0h4nJycTFRXFrbfemuMPwxNSU1NZtmwZ7du3V5mDj9I99H26h75N9883ffopjBhh1ouGhKTy9tvLeP759owdG0CnTuY5a9bAHXc4f31769fMuDiCCJIwLBZsQ4cSPHo0zYKDHc6zWqFuXTh0yPlH/hYLRETAtm3ZdwzIbizpff75lTvzWpC/h/ZPyl1RaMPr6tWrOXbsGFWqVEk7ZrVaefLJJ4mPj2f//v1OXxcUFERQUFCm4wEBAQX6P4IFfT3xPN1D36d76Nt0/3yHfeFTxjC5b18AXbsGsGSJ2b6qVSsoV84MuPZzS/IvU3iSR5lrvqbYtVRZnoB/q+ZOe7YGBMDLL5vXA8dr2ue6XnoJMmTeTJyNJT2LxWy91arVld02q6B+D925RqHdYevBBx9k27ZtbNmyJe1PeHg4Tz31FF9//bW3hyciIiK4t/DJ39/swwpmOGzDd2ynblpwfYPBbPu/rfi3ap7tNWNjzeYDERGOxyMjSQvKOck4lvTsj+Pjr+zgWlh5deb1zJkz7N27N+3xvn372LJlC2XLlqVKlSqUK1fO4fyAgAAqV67M9ddfX9BDFRERESdWr87caiq99Kv/Y2LMYPnRO2c41f8Zep81W2XuI5oRFeZz/6wYOru4wUBsLHTunLfNBewheMgQx+8hMtIMru5udiAFw6vhdcOGDbRp0ybtsb1WtVevXiQkJHhpVCIiIuKqw4fdPG/1ajq/0BvO/gHAnrb9OTLsFRZ3KIW/v3u7Xfn7m4E4LzwRgqVgeTW8xsTE4E6zg6zqXEVERMQ7wsJcOy+i7HkY+pz5Wb1hQFQUzJtH9fbtqf7fOYmJzmdBc7Plqzs8EYKl4BTamlcREREp/Fq2NANmVo2BLBa4q+JPtBzcwPws3jCgXz/Yvh3at087T7tdiasUXkVERCTXslv4FMwFXjJGsPR4cyy//Qbh4fDFF/DWW1C6dNp52u1K3KHwKiIiInnibPX/VXv3ss56E08zGYvNBg89BDt2QMeOmV7vzqIvkULb51VERER8h33h05rvLlJuxmhqPf0yfjYbVKoEs2ebT2bB7UVfckVTeBURERGP8N++hdbDe5nbWwG2bt3wmzHD3A0gG64u+nL1PCnaVDYgIiIieZOaCuPGQePGsG0bRvny/Pz001j/978cgyu4tugrKso8T0ThVURERHJvxw5o2hReeAEuXYLYWC5t2cLhZs1cfgvtdiXuUHgVERER9126BC+9BDfeCBs3QpkysGiRuXKrYkW3384TW77KlUE1ryIiIuIW685fOXtvb0J/WQeAccedWObOyXNRqna7Eldo5lVERERcY7WyrfdrpNZpSOgv6/iH0vQigSpbPiFxrWdWU9l3u+re3fyq4CoZKbyKiIhIzvbu5XjdGOoteJJgLvAVHajDDv6PXiQdsmgXLCkwCq8iIiKSNZsN3nwTo359yv+yhmRK8TBz6ciXJBEJaBcsKVgKryIiIuLcvn3Qti0MGoTl3DmWcwt12c48HgYc2wJoFywpKAqvIiIi4sgwzF2x6tWDlSuheHHW95lOe5bxF1dn+1LtgiX5Td0GREREBKvVnDU9veMALRc8TNkN35hPtGwJ8+dz9sA1GPNzfh/tgiX5TeFVRETkCmIPqelbUX38MQwZbNAuKYF44ihNMhcIZnefSdR/azD4+dEy2uy5mpR0ucY1PYvFfL5lS7NMNqdrqouA5JbCq4iIyBUiMRGGDIGDBy8fK1cOAk8cYg6PciefA7CWm+lDAr8lXM+SO83+q/ZdsLp2NYNq+gCbcRes9OHV2TUjI8330sYDkhuqeRUREfFxVqtZmrp4sfnV2Yr/xEQzeKYPkWBw24n/sZPa3MnnpBDI07xMC9awm+sBxw4C7u6C9emnzq5pzt6qtZbklmZeRUREfJgrM5tWq3lO+tnSihxlFo9xN0sB2MCN9GIBu6iddk76DgIxMeYxd3bBGjHCeYmBYZiztXFx5nuphEDcofAqIiLio+yzqRkDon1m0z4bunq1Y7i9l/eZwQDKc4KLBDCWF3iZEVwiwOl1li/PHFTtYTY7SUlZP+csGIu4QuFVRETEBzmbTbWzz2wOGQKlS8PSpebxchxnOgO5j/cB2EJ9erGAbdTP9lrjx1/+u6frVdVaS9yl8CoiIuKDMs6mZmQY5vPt2pmPO7OU2fSnEse4hD8TeJ4JPE8qgW5dN+Osbl6ptZa4Swu2REREfJCrM5ZlOMk7PMBS7qYSx9hBbW5iHaMZ43ZwBfe2go2IuNyJICOLBaKizDIEEXcovIqIiHiRK50CnHFlxvJ2PmcHdXiAhVjxYxLPcCMb2cSNaefYw2W5cq6P2dWtYF9+2fEaGa9pb60l4g6FVxERES9JTIToaGjTBnr0ML9GR2fdQip90LVazfpTZzOboZxmHn35nDsJ5zC/cj3N+YHnmMRFghzOjYyEDz+Eo0dhxQpYtAhGjnRt/DnN/nbq5F5rLRFXqOZVRETEC1ztFJD+fGcbDNgXZ9nfpz3fMI9+RHEQGxZeZygjGc8FQhyu88QTcM89jm2u7Kv+V650XKSVFVdmf91prSXiCoVXERGRAuZKp4D0PVCzCronT5pfy5aFlBP/8gpP8RizAdjLNfQmgR9o4XQM99yTdYuqli1d3wrWFa621hJxhcoGRERECpgrnQIOHIDRo80eqzkF3VssKzhWuV5acH275CAasNVpcHVloZR9K1j7+RlfD6pXFe9ReBURESlgrnYKGD/ebHWVVdAtzlmmGoN4//gthBzZbxbMrljBVQve4JylRJ6Cp7tbwYoUFJUNiIiIFDBP9DZtzhoS6M21/A7AnraPUf2jyVCqFLGYAdPZtrHx8a4HT9WrSmGk8CoiIlLAcqopzU4w5xnPSIbyOn4YHCCSfszjuZG3Ur3U5fM8FTxVryqFjcKriIhIAbBaHYPk669Dt26OnQJy0oR1LKAXNdgNwDz68iSvERpV2mkNq4KnFEUKryIiIvnMWZuryEgYPtzs2Zrd4i2AQFIYzWieZjL+2DhEGI8wly8tdwDwdrw+ypcrh8KriIhIPsqun+urr8J770GFCmZXAWe9VW9gIwvoRR12AvAODzCYN/iHMkS5WcMqUhQovIqIiOQTV/q5Pvkk7Ntn1qMmJFyugw3gIiMZz3NMpBhWjlKR58vNpvt7XZhxTIun5Mql8CoiIvKfjHWpeQ2HrvZzXb3arE2dOtWcpa3PVhLoRQO2AvAe3XiC6cyeU562bXM/HpGiQH1eRUREMD/ej46GNm2gRw/za3S0eTy3XO3naj8vtlMqO+4bx3oa0YCtHKcc3XiPp6LeY/aH5VUeIIJmXkVERLKtS+3aNfdN+V3t5xoWBuzcCb16UWvjRgD+btGFNT1nMaBGJRarPEAkjWZeRUTkipZTXSpAXJx5nrvs/Vwz7nRlZ7HA1ZFWWq19GW64ATZuhDJl4H//o8KqRO5+rBIxMQquIukpvIqIyBXNnbpUd/n7m3WskDnAWixwnbGbjcVb4PfcM3DxItxxB+zYAT17Zp14Ra5wCq8iInJFc7cu1V2xsWbZQUTE5WN+WHmx9OvsDGxAud9+gtBQePtt+PRTCA/P3YVErhCqeRURkSuaW3WpuZR+q9Z/t/xOy7d7c9X2NeaTt94Kb70FUVG5v4DIFUThVURErmj2ulR7f9WMLBbzeWfbr7rD32IjZudMeP5pOHcOSpaEKVPgkUdUIiDiBpUNiIjIFS2nulQwM+bq1eZWritX5mLx1v790L49PPGEGVzbtIHt2+HRRxVcRdykmVcREfE5nt5MwF6XOmSI4+KtyEi4/34YNizz8alTXWifZRgwd665jdaZM1C8OLz8MgwYAH6aPxLJDYVXERHxKYmJzkOmS2EyG+nrUu2h+Phx6NYtl/1fDxyAhx+Gb74xH7doAfPnw7XX5n6QIuLdsoFVq1bRqVMnwsPDsVgsLF26NO251NRURowYQd26dSlRogTh4eE89NBDHDp0yHsDFhERr7JvJpCxtZU9TOZlNywwZ29jYqB7d3M2d+jQXPR/NQxISIA6dczgGhwMr71m1hsouIrkmVfD69mzZ6lfvz7Tp0/P9Ny5c+fYtGkTo0aNYtOmTSQmJrJ7927uuusuL4xURES8LT83E3AmV/1fDx+Gu+6CPn0gORluugm2bDFTsHYaEPEIr5YNdOzYkY4dOzp9rnTp0ixbtszh2JtvvkmTJk3466+/qFKlSkEMUURECgl3wmRMTN6v51b/V8MwV3M98QScOgWBgTB2rFnrWkwVeiKe5FO/UadPn8ZisXDVVVdleU5KSgopKSlpj5OTkwGzDCE1NTW/h5h2jYK4luQP3UPfp3vo27K6f4cPQ0hIzq8/fBg8cesrV3btelFBx7DdPRC/jz8GwHbDDVjnzYPatc1QewX+O9TvoO8r6HvoznUshuHsA5iCZ7FY+Oijj+jSpYvT5y9cuEDz5s2pUaMGCxcuzPJ9Ro8ezZgxYzIdX7RoEcWLF/fUcEVERAj/4QfqzZ5NUHIytmLF2N2tG3tiYzE02yrilnPnztGjRw9Onz5NaGhotuf6RHhNTU3lnnvu4eDBg6xcuTLbb8rZzGtUVBTHjx/P8YfhCampqSxbtoz27dsTEBCQ79cTz9M99H26h74tq/tntULdunDoUNabCUREwLZtnisv/fRTePBB8+/pr1me40y5OIR7rR8AsM1Sl0cC3+ZkVH1efhk6dfLM9X2Vfgd9X0Hfw+TkZMqXL+9SeC30/2mYmppKt27d+PPPP/nuu+9y/IaCgoIICgrKdDwgIKBAf4EK+nriebqHvk/30LdlvH8BAWaL1K5dzcfpw6S9z/9LL5mL+z3F3gYrfWuuu/iYuZZHqWgc4xL+TOQ5xhsjSU0JxPK7Cy20riD6HfR9BXUP3blGoe6QbA+ue/bs4dtvv6VcuXLeHpKIiHiRfTOBiAjH45GR7gdGq9XsXpXTrlmxseYGWas/OcUfLR7iY7pQ0TjGTmpxMz/xImNJJRDIn64HIuLIqzOvZ86cYe/evWmP9+3bx5YtWyhbtixhYWF07dqVTZs28dlnn2G1Wjly5AgAZcuWJTAw0FvDFhERL3K2mYC7O2y5u9GB/zdf0uKxh+HQIQw/P162PcVoRpNC5mleT3c9EBFHXg2vGzZsoE2bNmmPhw0bBkCvXr0YPXo0n3zyCQANGjRweN2KFSuI0f8iiIhcseybCeSGfaMDl3bNOn3abHc1b575+LrrWNZzAc++eHOO13G11ZaIuMer4TUmJobs1osVkrVkIiJSROS00YHFYn7k37kz+K/4Fvr2NadRLRZzo4Hx4wlc50L/LMwZYRHxvEJd8yoiIuJJrmx0cPLAGY7EPg7t25vB9Zpr4PvvYcoUCAmhZUuzxMC+SCwjiwWiosxSBhHxPIVXERG5YuT0UX5rVrKdukR8Mss88MQTsHWrQxL19zdrYyFzgLU/jo/XbrAi+UXhVURErhhZfZRfnLNMZTAraUNV9nOh0tWwfDlMmwYlSmQ635NdD0TEPYW+z6uIiIin2D/yT0q6XPfajB9IoDfVMbvfLCzxKPf/+ipcVSrb9/JE1wMRcZ9mXkVE5IqR/iP/EM7zCsNZTUuqs5eDRHAbXxHyf7PxzyG4pn+/mBjo3t38quAqkv8UXkVE5IoSGwvLJ/3M1mI3MJwp+GEwn97cFrGDRz/soI/8RQo5lQ2IiMiVIyUFxoyhzcsvg81GStnK/NRnDlXv7MRWfeQv4hMUXkVE5MqwaRP06gU7dpiPe/Yk6I03aF22rHfHJSJuUdmAiIgUbRcvwosvQpMmZnCtUMHcZut//wMFVxGfo5lXEREpurZtM2dbt2wxH997L0yfbgZYEfFJmnkVEZGi59IlmDABGjUyg2u5cvDee/D++wquIj5OM68iIlK07NplzrZu2GA+7tIFZs2CSpW8OiwR8QzNvIqISNFgtcLkydCwoRlcr7rKrGtNTFRwFSlCNPMqIiK+77ffoHdvWLvWfNyxI8ydm3n/1gJgtWrXLZH8pJlXERHxXTYbxMdD/fpmcC1VCubNg88/90pwTUyE6Gho0wZ69DC/Rkebx0XEMzTzKiIivun336FPH3OaE6BdOzO4VqnicFpBzYQmJkLXrmAYjseTkszjS5ag3btEPEAzryIi4ltsNpgxw5xtXb0aSpQwF2R9802m4FpQM6FWKwwZkjm4wuVjcXHmeSKSNwqvIiLiO/78E269FQYOhLNnISYGtm+H/v3BYnE41T4TevCg41vYZ0I9GWBXr858nfQMAw4cuDxJLCK5p/AqIiKFn2HAW29B3bqwfDmEhMAbb5h/r1oVMGc1V66ExYvNwwU5E3r4sGfPE5GsqeZVREQKLasVfk48SPT4hwnb9rV5sHlzmD8fqldPOy8x0Qyr2c1+ppd+JjQmJu/jDAvz7HkikjXNvIqISKH06ScGwyssoGa3OoRt+5oLBDG29BQSh3yfKbg6Kw9whadmQlu2hMjITJULaSwWiIoyzxORvFF4FRGRQifo5EmC7ovl9VO9uYrTrKMJDdjC6ORhdL3PP61eNbuFUq7w1Eyovz9MnWr+PWOAtT+Oj1e/VxFPUHgVEZHCwzAwFr3LLYMHc4f1c1II5Bkm0Zwf2E2NTPWqOS2Uykp+zITGxprtsDK2l42MVJssEU9SzauIiHid1Qo/fXKMKpMeJ2q9Oa262dKQB43/Yyd1HM5NX6+am4/983MmNDYWOnfWDlsi+UnhVUREvCoxEb5+ZAnjTz5OBY6TSjH2dr+X1h+9xb8Ximf5Ons4dFdkpBlc82sm1N/fM4vARMQ5hVcREfGazxac4GLvgczmPQC2Uo/+QW/x7H2HuLQ0INvX2mc1IyPN3q3O6l4tFvNj/IQEOHZMM6EiRYHCq4iIeIV16Sc06fsoFTnKJfyZxLOMYxTF/CzAoSxfZ7GYgdUeQqdONbsNWCyOAdZeHjB1KrRtm7/fi4gUHC3YEhGRgnXqFDz0EP53d6ai7Si7qElT1vIC40glMNuXOqtX1UIpkSuLZl5FRKTgfPklPPwwHDqEzeLHK8ZwXmQMKQS79PKs6lW1UErkyqHwKiIi+S85GYYNg3nzzIeVq/PlfQt4ZmrTHF/6+utQqVLOgVQLpUSuDAqvIiKSv779Fvr2hQMHsGFhKkN4/sgEzk8tjr+/2SbLGXsv1kGDNIMqIpcpvIqISP44cwaefhpmzgTgd6rRh/msplXaKVkFVzvtSiUiGWnBloiIeN7330O9emnBdUGJAdRnq0NwTc9ZQH3nHS22EpHMNPMqIiKec+4cPPssvPGG+bhKFbYMfpvew7PvVWW1Xq5trVzZLJHt1KkAxisiPkczryIi4hk//AANGlwOro88Atu380u4a01WK1WC7t2hRYv8G6KI+D6FVxERyZvz52H4cLMVwJ49ZsPVr76COXMgNNTlLVxzs9WriFx5FF5FRCT3fv4Z44YbYMoUMAyOdOiFdesO6NAh7RT7Fq72DQYysncVaNmygMYsIj5N4VVERNyXkgLPP4/RtCmWX3/lMJW5i48J+zqB6AZXkZh4+VT7Fq6QOcA62zFLRCQ7Cq8iIlcAqxVWroTFi82vObWoytamTdCoEUyciMVmYxHdqcMOPuUuAJKSoGtXHAKstnAVEU9RtwERkSIuMRGGDIGDBy8fi4w0Z0PdCo2pqTBhgvnn0iWO+1Wgv20midzjcJphmDOqcXHmlq32GVVt4SoinqDwKiJShCUmmrOghuF43D476vKs5/bt0KsXbN4MwN+t7qH2qhn8TUWnpxsGHDhgBtX0W7ZqC1cRySuVDYiIFFFWqznjmjG4wuVjcXE5lBBcugQTJ8KNN5rBtWxZWLyYb/t/kGVwTe/w4VwNXUQkS5p5FREpYqxWc8Zz+XLHUoGMspodTfPLL+Zs6/r15uO77oLZs6FyZcJWujYWtb8SEU9TeBURKUKc1bfmJNPsqH27q5Ejza4CpUvDtGnwwANp7QHs7a+SkpzP7Fos5vNqfyUinqayARGRIsJe3+pOcIUMs6N79kCrVvDUU2Zw7dgRdu6EBx906HOl9lci4i0KryIihZwrba6yq2/NisPmADabmUbr14cff4RSpeCtt+DzzzP3t/qP2l+JiDd4NbyuWrWKTp06ER4ejsViYenSpQ7PG4bBCy+8QFhYGCEhIbRr1449e/Z4Z7AiIl6QmAjR0dCmDfToYX6NjjaPpw+106a5N+PqMDv65x/mG8fFmVu9tmsHO3ZAv35Zb4v1n9hY2L8fVqyARYvMr/v2KbiKSP7xas3r2bNnqV+/Pn379iXWyf/STZ48mTfeeIMFCxZQtWpVRo0aRYcOHdi1axfBwcFeGLGISMHJrs3VPfdAuXJw4kTu3jsyEuJfsxF7dDbUewrOnoUSJeDVV6F/f6eh1b4QLGOPVrW/EpGC5NXw2rFjRzp27Oj0OcMwiI+PZ+TIkXTu3BmA//u//6NSpUosXbqU+++/vyCHKiJSoFxpc5Wb4DpyJLRtCy2v/gv/R/qaLQnArHOdPx+qVXP6Oo9tdCAikkeFttvAvn37OHLkCO3atUs7Vrp0aW666SbWrl2bZXhNSUkhJSUl7XFycjIAqamppKam5u+g/7tO+q/ie3QPfV9RuIdr1pjhNCTEM+9nsZi1qc8/Z1DsnQT87hoO//6LERKCbcIEbAMGgJ+fuYtWBp9+aq7XMgzH8Zw8aR4H6NTJM+OEonH/rnS6h76voO+hO9exGIY75f35x2Kx8NFHH9GlSxcAfvzxR5o3b86hQ4cIS7cUtlu3blgsFt577z2n7zN69GjGjBmT6fiiRYsoXrx4voxdRMQXBJ84QYPp06m0aRMAJ2rUYPPgwZwND/fyyETkSnfu3Dl69OjB6dOnCQ0NzfbcQjvzmlvPPvssw4YNS3ucnJxMVFQUt956a44/DE9ITU1l2bJltG/fnoCAgHy/nnie7qHvKwr3cM0auOMOz7xXZITBoo7/48b/G4bl9GmMoCBsY8cSOngwrbPoZWW1wtq15oKwV17J+Rqffw4tWjh/jyNHoHJlaNrUtdZZReH+Xel0D31fQd9D+yflrii04bVy5coAHD161GHm9ejRozRo0CDL1wUFBREUFJTpeEBAQIH+AhX09cTzdA99ny/fw1atzAVZWW0CkJPXX4dKleDqoCM0TeiPZdon5hNNmmBZsAD/GjXIKkfmZqODI0cg/Y/aEzWyvnz/xKR76PsK6h66c41C2+e1atWqVK5cmeX2xQSYqXzdunU0bdrUiyMTEcl/2W0CkB1779ZBTxh0t7xLs0dqY/n0EwgMhEmT4IcfoEaNLF/viY0OsnqPpCTzeGKie+8tIpKeV2dez5w5w969e9Me79u3jy1btlC2bFmqVKlCXFwc48ePp3r16mmtssLDw9PqYkVEijL7JgAZZzDtLbIsFsdZWXvInTHmb/zvfxw+/NA80LAh/N//QZ06ma6Rvv1VxYq52+gg/TawOXVJsFjMdrKdO2v3LRHJHa+G1w0bNtCmTZu0x/Za1V69epGQkMDTTz/N2bNnefTRR/nnn39o0aIFX331lXq8isgVIzbWDHoZ+6t+/LHzj+Xfv+9Dbh7xOPz9NxQrBqNGwbPPOn6m/5/clAek52wb2NWrs38/w4ADB8zz1BtWRHLD5fB66NAhwj28IjUmJobsmh1YLBbGjh3L2LFjPXpdERFf4mwTgIyhNqr4CZq9Owi/VxebJ9StCwsWmLOuTmS1AYI7IiPN4Jq+hvXwYdde6+p5IiIZuVzzWrt2bRYtWpSfYxERETfYQ233kp/S4rE6+L272OzV+txzsH59lsE1u4/2XTFyZNbbwKavfc2Oq+eJiGTkcnidMGEC/fv359577+XkyZP5OSYREXHFP/9A795w113mcv8aNczeVBMmgJOuK3Y5fbSfFftisNGjzdDsrGa1ZUtzRjarRWb297DXyIqIuMvl8DpgwAC2bdvGiRMnqFWrFp9++ml+jktERLLz1VfmAqwFC8xEOHw4bN4MTZrk+NLcfGTvrL7Vmey6JLj6HiIi2XFrwVbVqlX57rvvePPNN4mNjaVmzZoUK+b4Fpv+27lFRETyQXKyGVTnzjUfV68OCQnQrJnLb5Gbj+yd1bdmJasuCe68h4hIVtzuNvDnn3+SmJhImTJl6Ny5c6bwKiIi+WT5cujbF/76y3w8ZAhMnAhubn1t/2g/qw0QLBaIiDAz8bFjlzscuDNbmlWXBM24ikheuZU8586dy5NPPkm7du3YuXMnFSpUyK9xiYiI3ZkzMGIEzJhhPq5aFebPh9atc/V29o/2u3bNulfs1KnQtm3ehu2sS4KISF65XPN62223MWLECN58800SExMVXEVECsKqVVC//uXgOmAAbNuW6+BqZ/9oPyLC8XhkpHlcH+2LSGHl8syr1Wpl27ZtREZG5ud4RER8UvqdqjzyEfm5c2bLqzfeMKdGq1SBefOgXTuPjVkf7YuIL3I5vC5btiw/xyEi4rOc7VQVGWl+9J6rGcwffzRbYO3ZYz5++GGYMgVCQz0xXAf6aF9EfI1WW4mI5EFWO1UlJZnH33sPKlRwcWbzwgV44QUzqNpsEB4Ob70FHTvm+/chIuIrFF5FRHIpu52q7Me6dzfPs8tyRnb9eujVC375xXz80ENmX6kyZfJj6CIiPsvlBVsiIuLIlZ2q0gdXuDwjm5j434GUFHj+eWja1AyulSrBxx+bmw8ouIqIZKKZVxGRXMrNTlWGYbajiouDzlU249+3F2zfbj55//3w5ptQrpxHxykiUpQovIqIuMneWWDXrty93t9Ipc+BiVhuHg/WS1C+PMycaU7JiohIthReRUTc4KyzgDvqsJ0F9OIGNoMVs/h15kyoWNGj4xQRKapU8yoi4iJ7Z4HcBFd/LvEMk9jIjdzAZk5Shl0jF5k7Aii4ioi4TOFVRMQF2XUWcCZ9O6zr+ZUfaM4kniOQVD6hE7eG7+T60d0v78cqIiIuUXgVEXGBK50FAEaOhBUr4N13wR8rTzKFLTTgJn7mH0rTiwV04WOemxamnaxERHJBNa8iIi5wtbNArVr/7Vi1Zw9HavSh/K8/APAlt/EIc/GLimRJfC533hIREYVXERFXhIW5eF4lG0ybDiNGUP78eYxSpdjd/3X+adiX/4Vbst9hS0REcqTwKiLigpYtzd2xkpKc171aLNC08j5aj+0L3680D7Zti2XePGpcfTU1CnS0IiJFl2peRUSyYLXCypWweLFZ8/r66+bxjGusLBj0N2bx/am6WL5fCSVKwIwZsGwZXH11QQ9bRKRI08yriIgTzvq5RkbC8OFmmLUfj+QAC4P60SplGVwAWrWC+fOhWjWvjFtEpKhTeBURycDezzVjeUBSErz6Krz3HlQobxDy7nxuXDiUYmeTISQEJk2CQYPATx9qiYjkF4VXEZF0suvnahhmycDkuEP8XP8RLF9+YT7RtCkkJMB11xXoWEVErkQKryLi06xWsx718GGzI0DLlnl7v+z7uRr0MBYy7dAgLIf+gaAgGDcOhg1TCwERkQKi8CoiPiurutSpU3OfJbPq51qRo8ymP134GIAT1RpT7tMEs7GriIgUGBVmiYjPSL/6f+xYsy414yxpUhI8+GDur+Gsn2s33mMntenCx1wkgOeYwI45Pyq4ioh4gWZeRcQnOJtldSZ9rarVCgEB7l0nfT/XcsbfTGcg3fgAgM00oDcLOBVVj3Ex7r2vK5yVQKgaQUTEkWZeRaTQs6/+zym42tkD7Nq17l/L398sO7jbSGQntenGB6RSjNG8yE38zHZLPeLjPR8qExMhOhratIEePcyv0dHmcRERuUzhVUQKtexW/+fkyJFcXPDkSWI/7MmH3ENF/mY7dbiJdYxhNJWjAliyBGJjc/G+2cgqnCclmccVYEVELlPZgIgUatmv/s9e5cqZj2X70fxnn8Ejj5ip188P21MjOHnLizx1IsjjH+Pbx5GUBEOHZt+aKy4OOndWCYGICCi8ikghlD5g7trl/uvt27c2bep4PKvuBDMm/kOn74aavVoBatSABQvwa9KE1rn6DrLnav0umAH2wAHz5xETkw+DERHxMQqvIuJReV105E6wc8YeXMHxulntmlXn4Fc0fOhhIMl88bBhZu/WkJDcDSAHWY0jJ1m18BIRudIovIqIx2TXd9WVOtHcBrv0IiMhPt7xmLO62VIk8yrDeZS5AOwrdi1Vlifg36p57i+eg7zU7zpr4SUiciXSgi0R8Yi8LjrKTbCzz7KOGQOLFsGKFbBvH3Tq5HhexrrZNnzHduqmBdepDKbOpS2stuVfcHU2DldYLBAVlfedw0REigrNvIpInmUXPO3HHnsMzp+HiAjnpQS5CXb2WdaMs7o2m+Nj+0fuJTjDSzzDE0wHYB/R9GE+3xPjcF5+cff97eE8P1pziYj4KoVXEckzV4Ln33/DAw+Yf3dWSuBqsBs50tzYyp162rAwaMFqEujNNfwBwEwe4yle4SwlHc7LT+6+f1bhXETkSqbwKiJ55u6Mor2UIH3PVFeDXdu2bq66P3+eVh89x/dMxQ+Dv4iiH/P4lvZpp1gsZlDM74/m0+/elVV5RIUK8PrrWc9Qi4hc6VTzKiJ55u6Moj24xcWZJQdwOdil7xaQXm5qPy3r1kGDBvi9EY8fBvPoRz22ZwquUDAfzdt370p/3fTjsFhg1izo2dMM6AquIiKZKbyKSJ7lFDydSd+/FHIOduBGwLxwgVoLFuDfujX89huEh8MXX1Dmw7coFVna4dTISHjvPShbFhYvhpUrLwfq/BAba844R0Q4Ho+MJF927xIRKWpUNiAieWYPnl27mkHTnY4B6UsO7MHOWbstl2s/N2yg2EMPUf2XX8zHDz5oDq5MGWIxd6pK34f2+HFzh6vctvfKjdjYzONQiYCIiGsUXkXEI7IKnjnJWHKQ62B38aK5ucCkSVisVi5cdRXF3nqLYvfc43Cav//lmtnEROjWLXPYdlaT62npxyEiIq5TeBURj0kfPJOSzBnN48edz8Rmt0jK7WC3ZQv06gXbtgFgu/deVnTqRLu77sryJTm197JYzJrczp01IyoiUpio5lVEPMoePHv2NBcfgQdqWLOSmmrOtjZubAbXcuXg/fexLlzIxdDQbF+aU3uvjDW5IiJSOCi8iki+ydfFSTt2QNOm8MILcOkS3H037NwJ996bdsqaNVkvwnK1vVd+b1wgIiLuKdTh1Wq1MmrUKKpWrUpISAjXXHMN48aNw8jLxuciUqBiY2H/fnPr1vRbuOY6uF66BC+9BDfeCBs3QpkysHAhfPghVKoEwKefmqfecQf06AFt2kB0tOMWta6298rvjQtERMQ9hbrm9eWXX2bmzJksWLCA2rVrs2HDBvr06UPp0qUZPHiwt4cnIi7y2OKk3bvN2tZ168zHd94Jc+Y4JMzERLPBwKJFji/NuAgrpw0DCmrjAhERcU+hDq8//vgjnTt35o477gAgOjqaxYsX8/PPP2f5mpSUFFJSUtIeJycnA5Camkpqamr+Dvi/66T/Kr5H97AQslrxmzYNvxdewHLhAkbp0linTMF48EEzZf53r6xWGDECgoPNxyEhjvfQYoFnnoHbb7/c3uvBB83n0gfY9DW5Npv5RwqOfgd9n+6h7yvoe+jOdSxGIf4MfuLEicyZM4dvvvmG6667jq1bt3Lrrbfy2muv0bNnT6evGT16NGPGjMl0fNGiRRQvXjy/hywiHlbi8GEavvEG5f7r23q0YUO2DBjAhQoVvDwyERHxlHPnztGjRw9Onz5NaA4Lbgt1eLXZbDz33HNMnjwZf39/rFYrEyZM4Nlnn83yNc5mXqOiojh+/HiOPwxPSE1NZdmyZbRv356AgIB8v554nu5hIWGz4TdrFn7PPYfl3DmMkiWxvvIKRt++WW7ltWQJ9Otnzri+/fYy+vZtz/nzme/hvHlmCYGd1Qpr18KRI1C5srkOTO2xvEe/g75P99D3FfQ9TE5Opnz58i6F10JdNvD++++zcOFCFi1aRO3atdmyZQtxcXGEh4fTq1cvp68JCgoiKCgo0/GAgIAC/QUq6OuJ5+keetH+/dC3r7m6C+CWW7C8/TbFrr4625eFhcH585cfnz8f4DS8hoVB+lsbEGAu6pLCRb+Dvk/30PcV1D105xqFOrw+9dRTPPPMM9x///0A1K1blz///JNJkyZlGV5FxIcZBsydC08+CWfOQPHi8Mor8Nhj4JdzcxT7IqyTJ50/r0VYIiK+r1C3yjp37hx+Gf4Py9/fH5tWT4gUGKvV7JOaVb9UjzlwADp0gP79zeDasqW58cCAAS4FV7i8CMsZj22MICIiXlWoZ147derEhAkTqFKlCrVr12bz5s289tpr9O3b19tDE7kiJCaaW6im34kqMtIMiHnaYCA9w4CEBHMv1uRkCA6GSZNg8GCXQ2t6WY0rMtIMrh4bt4iIeEWhDq/Tpk1j1KhRDBgwgGPHjhEeHk7//v154YUXvD00kSIvMdFc1JRxSWfGfql5cuiQOdP62Wfm45tvNoPs9dfn6W07dYIvvoDPPzcXYYWFmRO5mnEVEfF9hTq8lipVivj4eOLj4709FJEritVqzrg660ViGOZH8HFx0LlzLgOhYZi7CAwaBKdOQWAgjBtn1rp6MGG2aOG4MEtERHxfoa55FRHvWL3asVQgI8MwS1RXr87Fmx89CvfcAw88YAbXG2+ETZvg6ac1NSoiIjkq1DOvIuIdhw+7dt7y5ea5Ln8s//775gKsEyfMKdEXXjC3xNL0qIiIuEjhVUQyCQtz7bzx4y//PduFXMePw8CBZngFqF8fFiwwv4qIiLhBZQMikom9X2oWG1k5ZV/IlZiY4YmlS6F2bTO4+vubs60//6zgKiIiuaLwKiKZpO+X6mqAtS/uiov7rxfsyZNmXevdd8OxY2aAXbcOxowxF2iJiIjkgsKriDgVG2u2w4qIcP019oVcOyd/DnXqwMKFZq/WZ56BjRvNxVkiIiJ5oJpXEclSbKzZDmv1anNh1q5djnWuGYVymtcZSr3n5psHrr/e7Nt6880FMl4RESn6NPMqItny94eYGOjeHdq2zfq89nzDDurQl/kYFgsMGwabNyu4ioiIRym8iojLnC3kKsm/zKI/39CBKA6yv9g12FasgilTICTEe4MVEZEiSeFVRFyWcSFXG75jO3XpzxwApjGIrQu24t+6hRdHKSIiRZnCq4i4JTYWPvrfWeYVH8R3tCWaP9lHNPdV+I6ID9+gc48S3h6iiIgUYVqwJSLuWbOGzi/0hrO/A7CnbX+ODHuFRR1KaXdXERHJdwqvIuKa8+dh5Eh4/XWzJ1ZkJMybR/Vbb6W6t8cmIiJXDIVXEcnZunXQqxfs3m0+7tPHDLGlS3t3XCIicsVReBW5Almtl3u3hoWZXQScfuSfkgKjR8PkyWCzmSfPmQN33lnQQxYREQEUXkWKNGch9eOPYcgQOHjw8nmRkWYXgdjYdC/euNGcbd2503z8wAPmSWXLFuj3ICIikp7Cq0gRlZiYOaSWKwcnTmQ+NykJunY1t4ONvfOiuY3WxIlm+q1YEWbNgrvvLrjBi4iIZEHhVaQISkw0w6hhOB53FlzBPM9igVmPb6X9070o9ftWAGxd78VvxnSoUCGfRywiIuIa9XkVKWKsVnPGNWNwzU4xUnneGMfnxxpR6vetHKcc3XiPq396n8TVCq4iIlJ4KLyKFDGrVzuWCuSkFjtZS1PG8QIBXGIpnanDDj6gW1o5QWJi/o1XRETEHQqvIkXM4cOuneeHlad5mU3cQCM2coqreIB3uJuPOEpl4PLsbVycOaPraVYrrFwJixebX/PjGiIiUrQovIoUMWFhOZ9zHbtZQwte5hmCuMjn3E5tdrKQBwCLw7mGAQcOmDO6npSYCNHR0KYN9Ohhfo2O1iyviIhkT+FVpIhp2dJsfWWxZH7Ogo04XmcLDWjKT5wmlD68zZ18xmHCs31fV2d0XWFfUJaxvEFlCiIikhOFV5Eixt/fbMcKjgG2Gr+zkhheZxghXOBrbqUOO0igDxlnW51xZUbXFdktKMvvMgUREfF9Cq8iRVBsrNmzNSLCnG0dwHS2UY9WrCY1uCS2mbMJ+u4rJi+K4ttvs56pBfN4VJQ5o+sJOS0oy68yBRERKRrU51WkALm8LasHxMZC5/r7Sb63H2U2fweAEdOGgPlvQ3Q0MenOnTrV/LjeYnGcEbUH2vh4z43T1fIDT5YpiIhI0aGZV5ECkt0CJY+vujcMmDMH/wZ1zeBavDhMm4Zl+bfmRTNIP1ObXmTkf7tuxWZ6Sa65Wn7gqTIFEREpWjTzKlIAstrxKikJ7rkn87atkZHmbGiuQuPBg/Dww/D11+bjFi1g/ny49tpsXxYbC5075//MsH1BWVKS87pXi8V83lNlCiIiUrRo5lUkn7myQCnjtq25WnVvGJCQAHXqmME1OBimTDGncnMIrnb+/hATA927m1/zo6QhqwVl6R97skxBRESKFoVXkXzm7o5XkP2qe6clBocPw113QZ8+cPo03HQTbN4Mw4YVyhRYkGUKIiJStKhsQCSf5XbhUfpV9zEx5rHERHMW93IYNnii7GKmXHyCwDOnIDAQxoyB4cOhWOH+9S6oMgURESlaCvf/u4kUAXldeGQPvxnrZitwjJk8zj0nzdqCU9VuoMzHC8yyAR9hL1MQERFxlcoGRPJZdjteuWLXLli+3LFu9h6WsJPa3EMiqRTjBcZy48WfsNb0neAqIiKSGwqvIvksuwVKrhg/Htq1M0sFynKCxdzPEu6lAsfZSj0as55xjGLfwQA19hcRkSJP4VWkAGS1QKlcOfOrK6H2Lj5mJ7W5n/e4hD/jGElj1rOVBmnnqLG/iIgUdQqvIgUkNhb274cVK2DRIvPr0aPw4YeZQ216V3GKBTzEx3ShMkfZSS1u5ideYBypBDqcq8b+IiJS1GnBlkgBcrZAKf2q++XLzTIBu458wVweIYJDWPHjFZ5iNKNJIdjhPdTYX0RErhQKryKFgD3U2j/2D+U0rzGMfrwNwK9cT28SWMfNmV5rLzmYMkVtp0REpOhTeBUpRMLCoB3LmEc/qnAAGxbiieN5JnCBEKeviYyE++839yNIvxlCnraYFRERKaQUXkXygdWai1nQM2do9e5TxDALgL1cQx/ms4bLtQAWi1kfm5AAx46Z7338OHTrlnn7WfsWs9qxSkREihKFVxEPy7wLlguzoN9/D3364LdvHwBv8gTP8BJnKZF2ir08YOpUaNvW/LvVCtHRmYMrmMcsFnOL2c6dVUIgIiJFg7oNiHiQfRes9MEVLs+CJiZmeMG5c2bSjYmBffvg6qth+XLCP5xGmcgSDqdGRmaeRV29OvO10ku/xayIiEhRoJlXEQ+xWh13wUrP6SzoDz9A796wd6950qOPwiuvQGgosVzuQJBd6YGrfV3V/1VERIoKhVe5IuSqBtVNrs6C/vDteVotGwWvvWYejIiAefOgQweH85211crI1b6u6v8qIiJFhcKrFHm5qkHNBVdmN5uwjvp9e8OhX80DvXvD66/DVVfl6potW5rfS1KS8xlf9X8VEZGiptDXvCYlJfHAAw9Qrlw5QkJCqFu3Lhs2bPD2sMRHuF2DmgfZzW4GksJEnuVHmlH60K/myZ9+CvPn5zq4gjk7O3Wq+feMW8zaH8fHa7GWiIgUHYU6vJ46dYrmzZsTEBDAl19+ya5du5gyZQplypTx9tDEB+RUgwpmDarV6pnr2WdBM4bIhmxiA414lpfwx4atR0/YsQPuvNMj142NNRdyZdxi1tkCLxEREV9XqMsGXn75ZaKiopg/f37asapVq3pxROJL3FmJn1NtaVYy1tK+/rrZc9VigWLGRZ5nAs8zgWJYOUYF/nh6Nje/fHfuLpaN9FvMaoctEREpygp1eP3kk0/o0KED9957L99//z0REREMGDCARx55JMvXpKSkkJKSkvY4OTkZgNTUVFJTU/N9zPZrFMS1JHuHD0OI802pMp2X/na5eg8//RRGjDBLEOwiIsxj2xduY8LBftQ3tgLwZclYrG9Mo8MDFfL130bz5pf/brOZf65E+j30bbp/vk/30PcV9D105zoWw3D2oWrhEBwcDMCwYcO49957Wb9+PUOGDGHWrFn06tXL6WtGjx7NmDFjMh1ftGgRxYsXz9fxilisVqonJnL9e+/hd+kSF0uVYmv//hxq0cLbQxMRESm0zp07R48ePTh9+jShoaHZnluow2tgYCCNGjXixx9/TDs2ePBg1q9fz9q1a52+xtnMa1RUFMePH8/xh+EJqampLFu2jPbt2xMQEJDv15OsWa1Qty4cOpT1SvyICNi2zfHj9azuodUKa9ea7/fss+a2rOnVsO1i7sV+3GhsNM+/sxO2GdOhcuX8+PYkG/o99G26f75P99D3FfQ9TE5Opnz58i6F10JdNhAWFkatWrUcjtWsWZMPP/wwy9cEBQURFBSU6XhAQECB/gIV9PUks4AAePlls6sAOAZY+6Kql16C/yb4nbz+8j101m7Lzg8rw3iNcYwimBROcRWDmMbDw3oSE2XJ/AIpMPo99G26f75P99D3FdQ9dOcahbrbQPPmzdm9e7fDsd9++42rr77aSyMSX+OJlfhZtdsCqM5vrKYlr/A0waTwObdTm50s5AEOH1FwFRER8bRCPfM6dOhQmjVrxsSJE+nWrRs///wzc+bMYc6cOd4emviQvKzEz6rdlgUbg3mDSTxLCBc4TShDeZ359AHM0KpdrURERDyvUIfXxo0b89FHH/Hss88yduxYqlatSnx8PD179vT20MTHuLLVqjPO2m1V43fm04dWrAbgG9rzMG9xgCqAdrUSERHJT4U6vALceeed3OmhZu4i7kq/5asFG48xi8k8TUnOcoYSPMkU5vAo9tlW7WolIiKSvwp9eBXxJvtH/1X4k3n0ox3LAVhBDH15m/04bpoRGWkGV+1qJSIikj8UXkWy0bKFwfAy8xh1ahih/Ms5QhjBy0xnIMZ/6x0rVDB31oqI0K5WIiIi+U3hVSQrBw/i//jjvHLqawDW0JzeJPA71wKXSwRmzdJMq4iISEEp1K2yRLzCMIj67juKNWwIX38NQUFs6/UqPSO+Twuu4F67LREREfEMzbyKpHf4MP6PPMINn39uPm7SBBYsoF6NGvxhzV27LREREfEchVcRMBu5vvsuDByI36lTWIsVgxdfxP+ZZ6CY+WuS23ZbIiIi4jkKr1LoWfN7xvPYMRgwAP7bdtho2JDve/em5eOP419MvyIiIiKFiWpepVBLTIToaGjTBnr0ML9GR5vHPeLDD6F2bfNrsWIwZgyX1qzhX21BLCIiUigpvEqhlZgIXbtm3uEqKck8nqcAe+KEmYa7doXjx6FuXfj5Z3jhBQgIyNO4RUREJP8ovEqhZLXCkCFmKWpG9mNxceZ5bvvkE3O2dfFiDD9/dnR5nu+nbMBar2FehiwiIiIFQOFVCqXVqzPPuKZnGHDggHmey06dgocegs6d4ehRfitWkya2tdRdOp6YWwM9W44gIiIi+ULhVQqlw4c9ex5ffgl16sA772D4+TGZp6l3aRMbaJx2ir0cYelS8/GSJbByZS5nd0VERCRfKLxKoRQW5qHzkpPh4Yfh9tvh0CGM666jS7k1jOBlUgh2ONUwzD99+5qP+/XLhwViIiIikicKr1IotWxp7mBl34LVmQoVzNnSLGdHly83F2LNm2e+UVwcq6du5pO/m2Z77Yzv5ZEFYiIiIuIRCq9SKPn7w9Sp5t+zCrB//w0PPOBkdvTMGbNva7t28NdfUK2amXBff52kU8XdHkueF4iJiIiIxyi8SqEVG2vWnUZE5HyufXb0+3GroH59mDnTfGLAANi6FVq1AlwvR8goVwvERERExOMUXqVQi42F/fthxQr43//MUgFngo1zvG7E0fqF1vDHH1ClCnz7LUyfDiVLpp3nSjlCdlxeICYiIiL5QuFVCj1/f4iJMWdg//478/NN+ZEtNGAIZp3BoTsege3boW1bp++VUzlCdnI7cysiIiKeofAqPiPjrGcQF5jMU6yhBdexh4NE0IGv+L7nHAgNzfJ9sipH8PfP+toWC0RFmTO3IiIi4j3FvD0AEVeln/VszM8soBc1+RWA+fRmKK9zmqt41oXZ0dhYc6+C1avNUBwWZu4S261b5hlZ++P4+OwDroiIiOQ/hVcpNKxWxzDZsqVjWGzZEqpFpNAvaSwjeAl/bBymMo8yh8/oZM6ORro+O2ovR0hvyRIYMcLxWGSkGVxjY/Py3YmIiIgnKLxKoZCYCEOGOG4JGxlp1qfaQ6P/ts1s8u9FabYDsJAeDGIapyjrsdnR2FhzP4OvvzbbwzoL0SIiIuI9Cq/idYmJZpsrez9VO3v7qw/fTeXuXybC+PGUvnSJC6EVGBQwi7dOXJ4K9eTsqD2odu0KAQF5fz8RERHxHIVX8YicPvLP7nVDhmQOrmAeq8t2rn2gF6RuNg927UrwjBnMKluBnrm4noiIiPg2hVfJM1c+8s/K6tWOr7Pz5xJPM5nRjCYwNZXU0LIEzJ4O990HFgv+ZK5XFRERkaJPrbIkT+wf+WcMoPaP/NO2bM2Cs6b/NfiFH2nGRJ4nkFQ+5i4+e2kn3H9/7ncXEBERkSJB4VVyLaeP/AHi4szzspK+/ZUfVp7kVTbTkCas5x9K8yD/RxeWUqZmZY+OXURERHyTwqvkWlYf+dsZBhw4YJ6XFft2rdXZwypa8SpPEUwKX9CR2uxkoeVBoqIs2hxAREREANW8ShZcWYDl7CN/Z7I7z99i47P206g+/1mKc55kSjGU13mbvlj+KxHQ5gAiIiJip/Aqmbi6ACvMhZ2ssj3vjz+gTx/qr1oFwKqgdjyQMo8DVEm7pjYHEBERkfQUXsVBTj1Xlyy5HCbtH/knJTmve7VYzOczfeRvs8Hs2fDUU3D2LJQoAa++SvOH+/N/ayx5an+V25ZdIiIi4hsUXiVNTguwLBZzAVbnzmYg9Pc3Z2O7djWfS/+6LHe8+vNP6NcPli83H7duDW+/DdWq5bn9VV5adomIiIhv0IItSZObBVixseZsbESE47mRkY6ztBgGvPUW1K1rBteQEDPZfvcdVKuW57HntWWXiIiI+AbNvEqa3C7Aio01Z2Oz/Lj+4EF45BH46ivzcdOmkJAA113nkXG7O2MsIiIivkvhVdLkZQGWv7+Tj/wNA955BwYPhtOnISgIxo+HoUM9miLdmTHWrlwiIiK+TWUDksa+ACurTawsFoiKcrIAy5kjR6BLF+jVC06f5kS1xvw8axPWocM9Pv3piZZdIiIi4hsUXiWNfQEWZA6wWS7Aysgw4N13oXZt+OQTLhLAc0yg0h8/clOfWkRHe77+NM8tu0RERMRnKLyKA5cXYDnz99/QrRt07w4nT7KZBjRiA5N4Dut/FSr5sYDKozPGIiIiUqgpvEomsbGwfz+sWAGLFplf9+3LIbgmJpqzrUuWYBQrxpTQ0TThZ7ZTz+E0+6KquDhzoZUneGTGWERERHyCwqs4ZV+A1b27+dUe/KxWWLkSFi82v1qPnYAePeCee8yZ1zp12Dh9HcOTX+QSAU7f21nLrbzK04yxiIiI+Ax1GxCXZdwE4E4+pabfo1SyHQE/P3jmGXjhBfYkBrn0fp5eQJVjyy4RERHxeQqv4pL028aW5h/iiaM3C8AGv1CDIxMX0GZEE8C7C6ictuwSERGRIkNlAz4u08f4HqojzXgN+yYAHfiKHdShNwuwYeEVhnMDm+k1vUnatXNaQAVQoYK5eCu/xiwiIiJFk8KrD0tMhOhoaNPGLDtt04Z8aUW1ejWcPpjMHB7hKzoSSRJ7uJYWrOFpXuECwQ41rNktoLL7+2944IH8G7OIiIgUTQqvPsr+MX7GnaXyoxWVbdlytlOXR3gLgHiGUJ+trKWZw3npa1izWkDlTH6MWURERIomhVcflP5j/Iw82orqzBkYOJBbJrbjav7iD6rSmpUMJZ7zFM90esYa1vQtt/73P7NUwJn8aJ8lIiIiRZNPhdeXXnoJi8VCXFyct4fiVatXZ55xTc8jrahWrYL69WHGDAD+r8Tj1Gcbq2id6dTsNgGwL6CKiDBLBfJ1zCIiIlLk+Ux4Xb9+PbNnz6ZevXo5n1zEudpiKletqM6dM6dAY2Lgjz/MVPrNN5T8vxmctZTM9SYA+TpmERERuWL4RHg9c+YMPXv2ZO7cuZQpU8bbw/G6fGtFtXYtNGhgrrYyDHj4YdixA9q3z/MmAN5snyUiIiJFh0/0eR04cCB33HEH7dq1Y/z48dmem5KSQkpKStrj5ORkAFJTU0lNTc3Xcdqvk/5rfrj5Zrj2Wjh0yHndq8VihsybbwaXhnHhAn5jxuD3+utYbDaM8HCss2Zh3Hab+fx/b9KpE9x+u5lxjxyBypWhaVNzxjWn63h8zPmoIO6h5C/dQ9+m++f7dA99X0HfQ3euYzEMZ1Gi8Hj33XeZMGEC69evJzg4mJiYGBo0aEB8fLzT80ePHs2YMWMyHV+0aBHFi2deZHSlu2rPHm6YOpVS/xXR/tWmDTv69SO1ZEkvj0xERESuFOfOnaNHjx6cPn2a0NDQbM8t1OH1wIEDNGrUiGXLlqXVuuYUXp3NvEZFRXH8+PEcfxiekJqayrJly2jfvj0BAQH5eq1PP4URI8xWU3aRkfDSS+YsabZSUvCbMAG/V17BYrViVKqEdfp0jLvuKrxjLiAFeQ8lf+ge+jbdP9+ne+j7CvoeJicnU758eZfCa6EuG9i4cSPHjh3jhhtuSDtmtVpZtWoVb775JikpKfhnWCUUFBREUFBQpvcKCAgo0F+ggrhebCx07myu0D982KwXbdky+4VTAGzeDL16wfbt5uP778fy5psUK1cuX8ebpzF7QUH/mxHP0z30bbp/vk/30PcV1D105xqFOry2bduW7faA9Z8+ffpQo0YNRowYkSm4XonsrahckpoKkybBuHFw6RKULw8zZ5o7BBQgt8YsIiIikk6hDq+lSpWiTp06DsdKlChBuXLlMh2XHOzYYc62btpkPo6NNYNrxYreHZeIiIiIG3yiVZbkwaVL5mzrjTeawbVMGVi0yOxvpeAqIiIiPqZQz7w6s3LlSm8PwWOs1nyu/fz1V+jdG9atMx936gSzZ6uZqoiIiPgszbx6SWIiREdDmzbQo4f5NTraPJ5nViu89ho0bGgG19KlISEBPv5YwVVERER8msKrFyQmmmuk/mutmiYpyTyepwC7Zw+0bg1PPgkXLkCHDpfrXTPu7SoiIiLiYxReC5jVCkOGON9lyn4sLs48zy02G0ybBvXrww8/QMmSMGcOfPml2UhVREREpAhQeC1gq1dnnnFNzzDgwAHzPJft2wdt28LgwXD+PNxyiznb+sgjmm0VERGRIsXnFmz5usOHPXieYZizq8OHw5kzULw4vPIKPPYY+Ln+3yX5vnBMRERExEMUXguYq+ulcjzvwAHo1w+WLTMft2oF8+dDtWpZvsRZSP34Y7OMIf1scGQkTJ1qtoIVERERKUwUXgtA+tBYsaIZDpOSnNe9Wizm8y1bZvFmhmGG1KFDITkZQkLMPq6DBmU725qYmDmklisHJ05kPte+cGzJEgVYERERKVwUXvNZVqHRMMygmj7A2stT4+Oz+Nj+0CGzjvWLL8zHTZuaLbCuuy7HMXTtmjksOwuucHlscXHQubNKCERERKTw0IKtfJRVS6yTJ82vZcs6Ho+MzGK20zDgf/+D2rXN4BoUBJMnm9O5ToKr1QorV8LixbB8edbdDbKTq4VjIiIiIvlMM68eZLXCmjXm37//PvuWWBaL+Yn/t9/CsWPZLJQ6ehT69zeLUwEaNYIFC6BWLadjcDbTmxeuLjATERERKQiaefUQ+45Zd9xhPr7rrpxbYh08aIbV7t0hJsZJcH3vPXO29eOPISAAJkyAtWuzDa7OZnrzQhtyiYiISGGimVcPSF9TGhLi3mudzmwePw4DBsAHH5iPGzQwZ1vr1cvyfbLb/CA3clw4JiIiIuIFmnnNo7yGxkwzmx99ZM62fvABFCsGL74I69ZlG1wh580P3JHjwjERERERL1F4zaPchkaLBaKi0s1snjwJDzxgrtY6dswMsOvWwejREBiY4/vlpjbVHlLLlXM8nuXCMREREREvU9lAHuUlNKbNbH7+udkC6/Bhs1friBHmjGtQkMvvmZva1MhIcwydO2uHLREREfENCq95lJfQGNv2NPQdam46AHD99WZt6003uf2eLVvmvPlBRITZFtZZd4OYGPe/DxEREZGCpvCaR7kOjcu/gTr9zJoDiwWGDYNx49xf8fUff39zS9euXbPe/GDqVGjbNldvLyIiIlIoqOY1j+yhES6HRLuMobF7d4i58V/8B/SHDh3M4HrttbBqFbz6aq6Dq11srFmrGhHheFw1rCIiIlJUKLx6gMuh8bvvoG5dmDPHfDx4MGzZAi1aeHQs+/fDihWwaJH5dd8+BVcREREpGlQ24CGxsebCp1WrIDnZXIPVqtV/NaVnzsAzz8D06ebJ0dFmnWs+FZr6+6uGVURERIomzbx6kL//5UnUFi3+C66rV0P9+peD62OPwbZtbqdLqxVWroTFi82vVqsHBy4iIiLiIzTzml/OnzdbXsXHm6unoqLgrbfg1lvdfqvERHMjhPT9ZCMjzVpalQOIiIjIlUQzr/mgzO7dFGvUCF5/3QyuffvC9u25Dq5du2beCCEpyTyemOihQYuIiIj4AIVXT0pJwe/ZZ2n57LNY9uyB8HCz+HXePChd2u23y27rWfuxuDiVEIiIiMiVQ+HVk2w2/D75BIvNhq1HD9ixA26/Pddvl9PWs4YBBw6Y54mIiIhcCVTz6kkhIVjnz2fjZ59xw9ix+AUE5OntXN16Njdb1IqIiIj4IoVXDzOaNOHI8eMeeS9Xt57NzRa1IiIiIr5IZQOFmH3r2Yw7d9lZLGYTg5YtC3ZcIiIiIt6i8FqIubL1bHz8f/1kRURERK4ACq+FnMtbz4qIiIhcAVTz6gPsW8+uXm0uzgoLM0sFNOMqIiIiVxqFVx/h7+/2jrIiIiIiRY7KBkRERETEZyi8ioiIiIjPUHgVEREREZ+h8CoiIiIiPkPhVURERER8hsKriIiIiPgMtcoqZKxW9XMVERERyYrCayGSmAhDhsDBg5ePRUaaW8RqJy0RERERlQ0UGomJ0LWrY3AFSEoyjycmemdcIiIiIoWJwmshYLWaM66Gkfk5+7G4OPM8ERERkSuZwmshsHp15hnX9AwDDhwwzxMRERG5kim8FgKHD3v2PBEREZGiSuG1EAgL8+x5IiIiIkWVwmsh0LKl2VXAYnH+vMUCUVHmeSIiIiJXskIfXidNmkTjxo0pVaoUFStWpEuXLuzevdvbw/Iof3+zHRZkDrD2x/Hx6vcqIiIiUujD6/fff8/AgQP56aefWLZsGampqdx6662cPXvW20PzqNhYWLIEIiIcj0dGmsfV51VERETEBzYp+OqrrxweJyQkULFiRTZu3EirVq28NKr8ERsLnTtrhy0RERGRrBT68JrR6dOnAShbtqzT51NSUkhJSUl7nJycDEBqaiqpqan5Pj77NfJyrebNL//dZjP/SMHxxD0U79I99G26f75P99D3FfQ9dOc6FsNw1hq/cLLZbNx11138888/rFmzxuk5o0ePZsyYMZmOL1q0iOLFi+f3EEVERETETefOnaNHjx6cPn2a0NDQbM/1qfD6+OOP8+WXX7JmzRoiIyOdnuNs5jUqKorjx4/n+MPwhNTUVJYtW0b79u0JCAjI9+uJ5+ke+j7dQ9+m++f7dA99X0Hfw+TkZMqXL+9SePWZsoEnnniCzz77jFWrVmUZXAGCgoIICgrKdDwgIKBAf4EK+nriebqHvk/30Lfp/vk+3UPfV1D30J1rFPrwahgGgwYN4qOPPmLlypVUrVrV20MSERERES8p9OF14MCBLFq0iI8//phSpUpx5MgRAEqXLk1ISIiXRyciIiIiBanQ93mdOXMmp0+fJiYmhrCwsLQ/7733nreHJiIiIiIFrNDPvPrQejIRERERyWeFfuZVRERERMRO4VVEREREfIbCq4iIiIj4DIVXEREREfEZCq8iIiIi4jMUXkVERETEZxT6Vll5ZW+1lZycXCDXS01N5dy5cyQnJ2tLPB+le+j7dA99m+6f79M99H0FfQ/tOc2VFqlFPrz++++/AERFRXl5JCIiIiKSnX///ZfSpUtne47FKOK7ANhsNg4dOkSpUqWwWCz5fr3k5GSioqI4cOAAoaGh+X498TzdQ9+ne+jbdP98n+6h7yvoe2gYBv/++y/h4eH4+WVf1VrkZ179/PyIjIws8OuGhobqF9bH6R76Pt1D36b75/t0D31fQd7DnGZc7bRgS0RERER8hsKriIiIiPgMhVcPCwoK4sUXXyQoKMjbQ5Fc0j30fbqHvk33z/fpHvq+wnwPi/yCLREREREpOjTzKiIiIiI+Q+FVRERERHyGwquIiIiI+AyFVxERERHxGQqvHjZ9+nSio6MJDg7mpptu4ueff/b2kMRFkyZNonHjxpQqVYqKFSvSpUsXdu/e7e1hSS699NJLWCwW4uLivD0UcUNSUhIPPPAA5cqVIyQkhLp167JhwwZvD0tcZLVaGTVqFFWrViUkJIRrrrmGcePGubRfvXjHqlWr6NSpE+Hh4VgsFpYuXerwvGEYvPDCC4SFhRESEkK7du3Ys2ePdwb7H4VXD3rvvfcYNmwYL774Ips2baJ+/fp06NCBY8eOeXto4oLvv/+egQMH8tNPP7Fs2TJSU1O59dZbOXv2rLeHJm5av349s2fPpl69et4eirjh1KlTNG/enICAAL788kt27drFlClTKFOmjLeHJi56+eWXmTlzJm+++Sa//PILL7/8MpMnT2batGneHppk4ezZs9SvX5/p06c7fX7y5Mm88cYbzJo1i3Xr1lGiRAk6dOjAhQsXCnikl6lVlgfddNNNNG7cmDfffBMAm81GVFQUgwYN4plnnvHy6MRdf//9NxUrVuT777+nVatW3h6OuOjMmTPccMMNzJgxg/Hjx9OgQQPi4+O9PSxxwTPPPMMPP/zA6tWrvT0UyaU777yTSpUqMW/evLRj99xzDyEhIfzvf//z4sjEFRaLhY8++oguXboA5qxreHg4Tz75JMOHDwfg9OnTVKpUiYSEBO6//36vjFMzrx5y8eJFNm7cSLt27dKO+fn50a5dO9auXevFkUlunT59GoCyZct6eSTijoEDB3LHHXc4/C6Kb/jkk09o1KgR9957LxUrVqRhw4bMnTvX28MSNzRr1ozly5fz22+/AbB161bWrFlDx44dvTwyyY19+/Zx5MgRh/89LV26NDfddJNXs00xr125iDl+/DhWq5VKlSo5HK9UqRK//vqrl0YluWWz2YiLi6N58+bUqVPH28MRF7377rts2rSJ9evXe3sokgt//PEHM2fOZNiwYTz33HOsX7+ewYMHExgYSK9evbw9PHHBM888Q3JyMjVq1MDf3x+r1cqECRPo2bOnt4cmuXDkyBEAp9nG/pw3KLyKODFw4EB27NjBmjVrvD0UcdGBAwcYMmQIy5YtIzg42NvDkVyw2Ww0atSIiRMnAtCwYUN27NjBrFmzFF59xPvvv8/ChQtZtGgRtWvXZsuWLcTFxREeHq57KB6jsgEPKV++PP7+/hw9etTh+NGjR6lcubKXRiW58cQTT/DZZ5+xYsUKIiMjvT0ccdHGjRs5duwYN9xwA8WKFaNYsWJ8//33vPHGGxQrVgyr1ertIUoOwsLCqFWrlsOxmjVr8tdff3lpROKup556imeeeYb777+funXr8uCDDzJ06FAmTZrk7aFJLtjzS2HLNgqvHhIYGMiNN97I8uXL047ZbDaWL19O06ZNvTgycZVhGDzxxBN89NFHfPfdd1StWtXbQxI3tG3blu3bt7Nly5a0P40aNaJnz55s2bIFf39/bw9RctC8efNM7el+++03rr76ai+NSNx17tw5/Pwco4W/vz82m81LI5K8qFq1KpUrV3bINsnJyaxbt86r2UZlAx40bNgwevXqRaNGjWjSpAnx8fGcPXuWPn36eHto4oKBAweyaNEiPv74Y0qVKpVWz1O6dGlCQkK8PDrJSalSpTLVJ5coUYJy5cqpbtlHDB06lGbNmjFx4kS6devGzz//zJw5c5gzZ463hyYu6tSpExMmTKBKlSrUrl2bzZs389prr9G3b19vD02ycObMGfbu3Zv2eN++fWzZsoWyZctSpUoV4uLiGD9+PNWrV6dq1aqMGjWK8PDwtI4EXmGIR02bNs2oUqWKERgYaDRp0sT46aefvD0kcRHg9M/8+fO9PTTJpdatWxtDhgzx9jDEDZ9++qlRp04dIygoyKhRo4YxZ84cbw9J3JCcnGwMGTLEqFKlihEcHGxUq1bNeP75542UlBRvD02ysGLFCqf/39erVy/DMAzDZrMZo0aNMipVqmQEBQUZbdu2NXbv3u3VMavPq4iIiIj4DNW8ioiIiIjPUHgVEREREZ+h8CoiIiIiPkPhVURERER8hsKriIiIiPgMhVcRERER8RkKryIiIiLiMxReRURERMRnKLyKiIiIiM9QeBUR8QFWq5VmzZoRGxvrcPz06dNERUXx/PPPe2lkIiIFS9vDioj4iN9++40GDRowd+5cevbsCcBDDz3E1q1bWb9+PYGBgV4eoYhI/lN4FRHxIW+88QajR49m586d/Pzzz9x7772sX7+e+vXre3toIiIFQuFVRMSHGIbBLbfcgr+/P9u3b2fQoEGMHDnS28MSESkwCq8iIj7m119/pWbNmtStW5dNmzZRrFgxbw9JRKT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regression/np_genPoints.py +++ /dev/null @@ -1,30 +0,0 @@ -import numpy as np -import csv - -# 定义回归方程参数 -w = 1.35 -b = 2.89 - -# 生成x值范围 -x_min = 0 -x_max = 10 - -# 生成100个在x轴附近的点 -x = np.linspace(x_min, x_max, 100) - -# 根据回归方程计算y值 -y = w * x + b - -# 添加一些噪声，使数据更真实 -y += np.random.normal(scale=0.5, size=y.shape) - -# 将x和y合并成一个二维数组 -data = np.column_stack((x, y)) - -# 将数据保存到CSV文件 -with open('data1.csv', 'w', newline='') as csvfile: - writer = csv.writer(csvfile) - # 写入表头 - # writer.writerow(['x', 'y']) - # 写入数据 - writer.writerows(data) diff --git a/linear regression/plt_print.py b/linear regression/plt_print.py deleted file mode 100644 index 245fe99..0000000 --- a/linear regression/plt_print.py +++ /dev/null @@ -1,23 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - -# 原始数据 -points = np.genfromtxt("data1.csv", delimiter=',') - -x = points[:, 0] -y = points[:, 1] - -# 拟合直线 -x_range = np.linspace(min(x), max(x), 100) -y_pred = 0.3880246877670288 * x_range + 1.7214288711547852 - 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end of file diff --git a/week1/C1_W1_Lab02_Model_Representation_Soln.ipynb b/week1/C1_W1_Lab02_Model_Representation_Soln.ipynb index 93b57fc..a08c3ae 100644 --- a/week1/C1_W1_Lab02_Model_Representation_Soln.ipynb +++ b/week1/C1_W1_Lab02_Model_Representation_Soln.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "d5947faf528d757", "metadata": {}, "source": [ "# Optional Lab: Model Representation\n", @@ -9,21 +10,21 @@ "
\n", " \n", "
" - ], - "id": "d5947faf528d757" + ] }, { "cell_type": "markdown", + "id": "428fc5f273aad770", "metadata": {}, "source": [ "## Goals\n", "In this lab you will:\n", "- Learn to implement the model $f_{w,b}$ for linear regression with one variable" - ], - "id": "428fc5f273aad770" + ] }, { "cell_type": "markdown", + "id": "7b23d466e8a13579", "metadata": {}, "source": [ "## Notation\n", @@ -41,34 +42,34 @@ "| $w$ | parameter: weight | `w` |\n", "| $b$ | parameter: bias | `b` | \n", "| $f_{w,b}(x^{(i)})$ | The result of the model evaluation at $x^{(i)}$ parameterized by $w,b$: $f_{w,b}(x^{(i)}) = wx^{(i)}+b$ | `f_wb` | \n" - ], - "id": "7b23d466e8a13579" + ] }, { "cell_type": "markdown", + "id": "d6756737db18e1c", "metadata": {}, "source": [ "## Tools\n", "In this lab you will make use of: \n", "- NumPy, a popular library for scientific computing\n", "- Matplotlib, a popular library for plotting data" - ], - "id": "d6756737db18e1c" + ] }, { "cell_type": "code", "execution_count": 1, + "id": "792fae4c5017098a", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "plt.style.use('./deeplearning.mplstyle')" - ], - "id": "792fae4c5017098a" + ] }, { "cell_type": "markdown", + "id": "f8e92dd846374938", "metadata": {}, "source": [ "# Problem Statement\n", @@ -83,20 +84,20 @@ "| 2.0 | 500 |\n", "\n", "You would like to fit a linear regression model (shown above as the blue straight line) through these two points, so you can then predict price for other houses - say, a house with 1200 sqft.\n" - ], - "id": "f8e92dd846374938" + ] }, { "cell_type": "markdown", + "id": "d877986e09640e0f", "metadata": {}, "source": [ "Please run the following code cell to create your `x_train` and `y_train` variables. The data is stored in one-dimensional NumPy arrays." - ], - "id": "d877986e09640e0f" + ] }, { "cell_type": "code", "execution_count": 2, + "id": "5306a3aeca0c549a", "metadata": {}, "outputs": [ { @@ -115,29 +116,29 @@ "y_train = np.array([300.0, 500.0])\n", "print(f\"x_train = {x_train}\")\n", "print(f\"y_train = {y_train}\")" - ], - "id": "5306a3aeca0c549a" + ] }, { "cell_type": "markdown", + "id": "1f2af3b208024cc1", "metadata": {}, "source": [ ">**Note**: The course will frequently utilize the python 'f-string' output formatting described [here](https://docs.python.org/3/tutorial/inputoutput.html) when printing. The content between the curly braces is evaluated when producing the output." - ], - "id": "1f2af3b208024cc1" + ] }, { "cell_type": "markdown", + "id": "8e8c52a75e71157d", "metadata": {}, "source": [ "### Number of training examples `m`\n", "You will use `m` to denote the number of training examples. Numpy arrays have a `.shape` parameter. `x_train.shape` returns a python tuple with an entry for each dimension. `x_train.shape[0]` is the length of the array and number of examples as shown below." - ], - "id": "8e8c52a75e71157d" + ] }, { "cell_type": "code", "execution_count": 3, + "id": "eca4f6257ac4de85", "metadata": {}, "outputs": [ { @@ -154,20 +155,20 @@ "print(f\"x_train.shape: {x_train.shape}\")\n", "m = x_train.shape[0]\n", "print(f\"Number of training examples is: {m}\")" - ], - "id": "eca4f6257ac4de85" + ] }, { "cell_type": "markdown", + "id": "a0f74fc14328cbb1", "metadata": {}, "source": [ "One can also use the Python `len()` function as shown below." - ], - "id": "a0f74fc14328cbb1" + ] }, { "cell_type": "code", "execution_count": 4, + "id": "314bdb01cd4b140b", "metadata": {}, "outputs": [ { @@ -182,11 +183,11 @@ "# m is the number of training examples\n", "m = len(x_train)\n", "print(f\"Number of training examples is: {m}\")" - ], - "id": "314bdb01cd4b140b" + ] }, { "cell_type": "markdown", + "id": "7954c480ba221f81", "metadata": {}, "source": [ "### Training example `x_i, y_i`\n", @@ -195,12 +196,12 @@ "\n", "To access a value in a Numpy array, one indexes the array with the desired offset. For example the syntax to access location zero of `x_train` is `x_train[0]`.\n", "Run the next code block below to get the $i^{th}$ training example." - ], - "id": "7954c480ba221f81" + ] }, { "cell_type": "code", "execution_count": 7, + "id": "b6a441b1c3984a36", "metadata": {}, "outputs": [ { @@ -217,36 +218,36 @@ "x_i = x_train[i]\n", "y_i = y_train[i]\n", "print(f\"(x^({i}), y^({i})) = ({x_i}, {y_i})\")" - ], - "id": "b6a441b1c3984a36" + ] }, { "cell_type": "markdown", + "id": "34065d7bc1038bf0", "metadata": {}, "source": [ "### Plotting the data" - ], - "id": "34065d7bc1038bf0" + ] }, { "cell_type": "markdown", + "id": "e89956448ccd316d", "metadata": {}, "source": [ "You can plot these two points using the `scatter()` function in the `matplotlib` library, as shown in the cell below. \n", "- The function arguments `marker` and `c` show the points as red crosses (the default is blue dots).\n", "\n", "You can use other functions in the `matplotlib` library to set the title and labels to display" - ], - "id": "e89956448ccd316d" + ] }, { "cell_type": "code", "execution_count": 10, + "id": "c1b08142e0243c78", "metadata": {}, "outputs": [ { "data": { - "image/png": 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yhanTlA0dO3bExcXlosv37t1bh9JERKQxVBv833//PYZh8D//8z+0adOGP/3pTxiGwZIlS3RyV0TkMuXQUE+PHj3YunVrpbYbb7yRjRs31riD9PR0PvjgA+bNm8c999yDi4sL7dq1Y968ebi5uRESEkLbtm0BeP311+nWrZt9XQ31iIjUXr3cwNWqVStef/11CgsLKSws5LXXXsPLy6vG9axWq/0Lw9fXl4yMDLKzs+nYsSOffPIJAP7+/mRlZZGVlVUp9EVExBwOBf/ixYvZvn078fHxxMfHs2PHDpYsWVLjerNmzSIlJQUAPz8/fH19AXB3d8fNzQ2AoqIiYmNjSUtL4+zZs5faDxERcZBpV/XYbDaSk5N59913iY6OJicnB4D8/HzuuOMO1q5di7u7O0VFRbRs2ZIXXniB5s2b8/DDD9u3oaEeEZHaq5ehnksxb948Ro8eXanNarWSkpLCW2+9hbt7xXnlli1bAjBy5EgsFotZ5YiIyC8cunP3UuTl5bFlyxZmzJjB9u3befXVV9m0aRMPPvigfSz/3LlzGIaBp6cnubm5dOrUyaxyRETkF9UO9YwaNYpFixbxv//7vzz55JOXvJPo6GimTJlCQkICvXv3BuCRRx4hKiqKxMREmjdvjp+fH/Pnz6dFixb29TTUIyJSe3W6c7dbt24sWbKEu+66i48++ojffzQ4OLgeS61KwS8iUnt1Cv7Fixczd+5ccnJyuOGGGyqv6OLCF198UY+lVqXgFxGpvXqZq2fy5Mk89dRT9VuZAxT8IiK1Vy/B//PPPzN16lT7JZnR0dE8+uijmp1TROQPqF4u57zvvvs4ceIEL730Ei+++CLFxcWkpqbWX5UiItJgHLqc8/vvv2fx4sX29+Hh4URERJhWlIiImMehI35vb29WrFhhf//xxx/j7e1tWlEiImIeh8b49+zZw8MPP8yWLVtwdXWlR48eTJ061fQbrjTGLyJSe3oCl4iIk2m0uXpEROSPScEvIuJkFPwiIk7GoeD/29/+RklJCVarlQEDBtC2bVtmz55tdm0iImICh4J/zZo1tGjRgmXLltG1a1f27NnDK6+8YnZtIiJiAoeC/+zZs1itVpYuXcrtt9/OVVddZXZdIiJiEoeCPy0tjfbt21NSUsKAAQM4cOBApXnzRUTk8nHJ1/GXlpbaH59oFl3HLyJSe3W6jt8wDN577z3uvfdeEhISSEhIICUlhXfffRdXV8cuCEpPTyc6OhqAKVOmEB0dTXJyMjabDYAFCxYQFRVFUlISxcXFDndMREQuTbXpfe+997Jq1SrGjBnDtGnTmDZtGvfccw+rV68mJSWlxo1brVa2bt0KwNGjR1mzZg05OTmEh4fz0UcfYbPZmDFjBtnZ2YwZM4Y333yzfnolIiIXVe1YzZdffsnOnTsrtXXp0oVBgwY59NjFWbNmkZKSwoQJE9i4cSNxcXEAxMfHs3DhQrp160ZYWBju7u7Ex8dz//33X3pPRETEIdUe8bdt25ZZs2Zx+vRpe9vp06eZOXMmbdq0qXbDNpuNtWvXMnDgQABOnDhhn9HTx8eH48ePX7BNRETMVW3wL126lO+++45evXrRrl072rVrR+/evdmxYwdLly6tdsPz5s1j9OjR9ve+vr72Mfzi4mJ8fX0v2CYiIuaqdqjH39+f9PR00tPTa73hvLw8tmzZwowZM9i+fTubNm1i48aNPPnkk2RmZhIZGUlwcDAWi4WysjJ7m4iImKvGyzm/+uorPv74Y/Lz84GK4Z9bbrmFG2+80eGdREdHk5OTw+TJk8nIyODaa69lzpw5NGnShHnz5vHGG2/g5+fHwoULK116pMs5RURqr07z8T/99NNs2rSJUaNGERgYCMChQ4dYtGgRvXv3ZvLkySaU/BsFv4hI7dUp+IODg6tc1QMV1/cHBweza9eueirzwhT8IiK1V6cbuFq0aEFmZmaV9szMTE3ZICJymar25O7ixYt56qmnSE1Nxd/fH8MwOHbsGDfccAOLFi1qqBpFRKQeOTRXj2EYFBYWAtCqVStcXFxMLww01CMicilqys4aZ1krKChg1apVla7qGTp0KG3btq3HMkVEpKFUO8Y/bdo0Bg8ezJ49e2jZsiUtW7Zk7969JCQkMG3atIaqUURE6lGNV/Vs374dDw+PSu1Wq5WwsLALXvFTnzTUIyJSe3W6qsfDw4O9e/dWad+3b5/pc/GLiIg5qk3vWbNmkZycjJubm/0Grh9//JHy8nJmzZrVIAWKiEj9cuiqnvz8fPvJ3YCAAAICAkwvDDTUIyJyKep8VQ9cOOwtFgvdu3evY3kiItLQHHt+4gXcfPPN9VmHiIg0kGqP+M+fT/98hmFQVFRkSkEiImKuaoN/1apVzJs3j+bNm1dqNwyDL774wtTCRETEHNUG/6BBg2jRogWxsbFVlvXp08e0okRExDwOXdXTWHRVj4hI7dXpBi4REbnymBb8FouFqKgoYmJiSE1NZfPmzcTFxREXF0fHjh155ZVXAAgJCbG3f/fdd2aVIyIivzBt3oWQkBDWr18PQGpqKqWlpWRlZQEwfPhwkpKSgIoHuv/aLiIi5nM4+M+cOcPhw4cpLS21twUHB1/08+dP7Obp6Un79u0BOH36NAUFBQQFBQFQVFREbGwsXbt2ZerUqTRt2rTWnRAREcc5NNTzwgsvEBISwn333UdaWhppaWk88MADNa63fPlyunfvzpEjR2jVqhUAK1euZOjQofbP5OTkkJ2dTYcOHZg5c+YldkNERBzlUPDPnTuXvLw8srKyWLNmDWvWrHHoOv5hw4ZhsVgIDAxkxYoVAHz44Yfcdttt9s+0bNkSgJEjR2KxWC6lDyIiUgsOBX9ISAhWq7VWGz7/897e3jRr1gybzcaOHTvo0aMHAOfOnbN/Ljc3l06dOtVqHyIiUnsOjfGfO3eOrl27EhUVhaenp7194cKFF11n1apVpKenA9C5c2cSEhL47LPPGDhwoP0zx48fJzExkebNm+Pn58f8+fMvtR8iIuIgh27gWrt27QXb+/fvX+8FnU83cImI1F6dpmUuLS3F3d2dfv361X9lIiLSKKoN/hEjRrBixQpCQkJwcXGxtxuGgYuLywUfyygiIn9smqtHROQKU6e5ej799NNqN37s2DE2b958iaWJiEhjqHaoZ+PGjTz33HP069ePiIgIWrdujdVqZe/evWRnZ2MYBi+88EJD1SoiIvWgxqEem81GZmYmGzZsoKCggKZNm9KlSxeGDBnC9ddfb2pxGuoREam9mrJTY/wiIlcYzccvIiKVKPhFRJyMgl9ExMk4FPybN28mMjLSPonat99+yzPPPGNqYSIiYg6Hgv/BBx9k0aJFeHt7AxAWFkZGRoaphYmIiDkcCv7y8nI6duxYqc3Nzc2UgkRExFwOTcvcuXNn+4NUCgoKmD59Or169TK1MBERMYdDR/wzZsxg3bp1uLm5kZSURGlpKdOnTze7NhERMYFDN3CVlZVVGdq5UFt90w1cIiK1Vy83cMXExFBcXFxpo7GxsfVQnoiINDSHgv/nn3+2X9EDFd8gp0+frnYdi8VCVFQUMTExpKamYhgGPj4+xMXFERcXR1FREQALFiwgKiqKpKSkSl8uIiJiDoeC39fXl+zsbPv77OzsSl8EFxISEsL69etZt24dAJs2bSIsLIysrCyysrJo2bIlNpuNGTNmkJ2dzZgxY3jzzTfr0BUREXGEQ1f1vPHGG6SkpHDq1CkAWrRowdy5c6tdx8PDw/7a09OT9u3bs2PHDmJiYrjpppt48cUX2blzJ2FhYbi7uxMfH8/9999fh66IiIgjHAr+rl27snHjRkpKSoCK4HfE8uXL+fvf/05wcDCtWrVi165d+Pn58cADD5CRkUGrVq3sfzn4+Phw/PjxS+yGiIg4qtrgnz59OuPHj+eZZ56p9MzdX9X0EJZhw4YxbNgwHnroIVasWMHIkSOBimf5bt68meHDh9vH9YuLi/H19b3UfoiIiIOqDf4OHToA0KVLl1pv2Gq14unpCYC3tzdNmjSxXwKam5tLWFgYwcHBWCwWysrKyMzMJDIy8hK6ICIitVFt8N96662UlZWRlZXF7Nmza7XhVatWkZ6eDlTc+du2bVv69OmDl5cX119/PRMnTsTNzY2xY8cSExODn58fCxcuvPSeiIiIQxy6gSsxMZEPPviAZs2aNURNdrqBS0Sk9mrKTodO7rZq1YqePXsyZMgQvLy87O160LqIyOXHoeAfPHgwgwcPNrsWERFpADUG/4cffsjRo0fp1q0bN998c0PUJCIiJqr2zt3777+fadOmUVhYyD//+U+effbZhqpLRERMUu3J3e7du7Nt2zZcXV05c+YMN910E998802DFaeTuyIitVen2TmbNGmCq2vFRxr6ih4RETFHtWP83377LQEBAQAYhkFhYSEBAQEYhoGLiwv5+fkNUqSIiNSfaoPfZrM1VB0iItJAHJqWWURErhwKfhERJ6PgFxFxMgp+EREno+AXEXEyCn4RESej4BcRcTIKfhERJ6PgFxFxMqYFv8ViISoqipiYGFJTU9m3bx8xMTHExsYyevRoysrKAAgJCSEuLo64uDi+++47s8oREZFfmBb8ISEhrF+/nnXr1gFw7NgxMjIyyM7OpmPHjnzyyScA+Pv7k5WVRVZWFt26dTOrHBER+YVpwe/h4WF/7enpSfv27fH19QXA3d0dNzc3AIqKioiNjSUtLY2zZ8+aVY6IiPzC1DH+5cuX0717d44cOUKrVq0AyM/PJzMzk4SEBABycnLIzs6mQ4cOzJw508xyREQEk4N/2LBhWCwWAgMDWbFiBVarlZSUFN566y3c3SsmBm3ZsiUAI0eOxGKxmFmOiIhgYvBbrVb7a29vb5o1a8b999/Pgw8+aB/LP3funP1zubm5dOrUyaxyRETkFzU+bP1SrVq1ivT0dAA6d+5MixYt+OCDD9i/fz9Tp07lkUceISoqisTERJo3b46fnx/z5883qxwREflFtc/cbWx65q6ISO3V6Zm7IiJy5VHwi4g4GQW/iIiTUfCLiDgZBb+IiJNR8IuIOBkFv4iIk1Hwi4g4GQW/iIiTUfCLiDgZBb+IiJNR8IuIOBkFv4iIk1Hwi4g4GQW/iIiTuTKDf+JEWLy44vXixRXvRUQEMDH4LRYLUVFRxMTEkJqaimEYTJkyhejoaJKTk7HZbAAsWLCAqKgokpKSKC4urvuOn3++4ic5GUaMqPj9a5uIiJgX/CEhIaxfv55169YBsGnTJtasWUNOTg7h4eF89NFH2Gw2ZsyYQXZ2NmPGjOHNN9+s204nTvzt6L68HJYtq/j9+2UiIk7MtOD38PCwv/b09GTnzp3ExcUBEB8fz4YNG9i5cydhYWG4u7vb2+okJARcL9IlV9eK5SIiTs7UMf7ly5fTvXt3jhw5QmlpKd7e3kDFMyCPHz/OiRMnqrTVyV13wa23XnjZrbdWLBcRcXKmBv+wYcOwWCwEBgbi7u5uH8MvLi7G19cXX1/fKm11sngxZGRceFlGxm8nfEVEnJhpwW+1Wu2vvb29KSsrY+3atQBkZmYSGRlJcHAwFouFsrIye1ud5OX9Nqb/e+XlFctFRJycu1kbXrVqFenp6QB07tyZSZMmcfjwYaKjo7n22mt59NFH8fDwYOzYscTExODn58fChQvrttPnngPDqDiJ6+paMbyTkVER+s89V/EjIuLkXAzDMBq7iIs5efKk/bWPj4/jK06cWHEi9667KoZ38vIU+iLiNGrKzisz+EVEnFhN2Xll3rkrIiIXpeAXEXEyCn4RESdj2lU99e38MSsREbl0OuIXEXEyCn4RESfzh76cU0RE6p+O+EVEnMwVE/z5+fn06tWLpk2bUlpaWmXZwIEDiYqKIjMzs5EqrH/V9XnixIn069ePfv368fnnnzdShfWvuj4DGIZBjx49mDVrViNUV/+q6+/Zs2f585//zMCBA3nooYcaqcL6V12f165dS9++fYmMjGTGjBmNVGH9+89//mN/cNVjjz1WaZkp+WVcIc6cOWMUFRUZ/fv3N2w2W6VlDz30kJGbm2uUlJQY/fv3b5wCTVBdn/fu3WsYhmEcP37ciImJaYzyTEKGLIUAAAg4SURBVFFdnw3DMD766CMjPj7eeOuttxqhuvpXXX8nT55sZGZmNlJl5qmuz7feequxf/9+o6yszLjxxhsbqcL6d/jwYePMmTOGYRjG6NGjjW3bttmXmZFfV8wRf9OmTfHz87vgsm3bttGvXz+aN29OixYtKCkpaeDqzFFdnzt27AhUPATHxcWlIcsyVXV9Bli0aBF/+tOfGrAic1XX36ysLJYvX05cXBzLly9v4MrMU12fQ0NDOXnyJFarFS8vrwauzDxt2rShadOmALi7u+Pm5mZfZkZ+XTHBX52ysjJ7+NXLA18uI88//zxpaWmNXUaDWL16Nf3798fd/bK5PaVO9uzZwy233MLHH3/MpEmTLjj0daUZMWIESUlJdOnSheTk5MYup95t27aNY8eO0a1bN3ubGfnlFMF//rdnvTzw5TLx4YcfUlhYyOjRoxu7lAYxa9YsUlNTG7uMBuPj40P//v3x8vIiKCiIn376qbFLMt0TTzxBTk4Ou3bt4p133uHnn39u7JLqTVFREePHj+ftt9+u1G5GfjnFoVF4eDhffvkl4eHhFBcX2x/3eCXbtm0br732Gh9//HFjl9Jgdu3axYgRIzh06BCGYRAdHU2XLl0auyzTREVFsW3bNnr16sUPP/yAv79/Y5dkOjc3N3x9fWnSpAmurq7YbLbGLqlelJaWcvfddzNlyhTatGlTaZkp+VUvZwr+AM6dO2cMGjTI8PX1NQYOHGhs2LDBGD9+vGEYhnHw4EFjwIABRmRkpLF69epGrrT+VNfnhIQEo3v37kb//v2NYcOGNXKl9ae6Pv9q9uzZV8zJ3er6m5+fbwwePNjo27evMWvWrEautP5U1+eVK1caN954oxEZGWlMnDixkSutPwsXLjRat25t9O/f3+jfv7+xfv16U/NLN3CJiDgZpxjjFxGR3yj4RUScjIJfRMTJKPhFRJyMgl9ExMko+OWyMWHCBEJDQwkLCyMqKopTp06Rn5/PPffcU+dtT5o0iVWrVgHw4osvcu2111a5nvrIkSPExsYSFBTEmDFjKCsrA+DMmTMMGzaMzp07k5iYyKlTpwAoLy/nL3/5C0FBQURFRZGfn1/nOm+//XZ69OjBggULePnll+3tu3fvJiUlpc7bF+eg4JfLwvr161m/fj1bt27l22+/5Z133sHDw4OAgADeeeedOm373LlzrFixgqFDhwIwePBgNmzYUOVzL774IsnJyezevRsPDw+WLl0KwMyZMwkNDWXXrl3069eP1157DYCMjAxOnz7N7t27eeCBB5g0aVKd6iwoKGDPnj1s3bqV5OTkSsEfFBTEsWPHOHjwYJ32Ic5BwS+XhcOHD+Pv72+fhycoKAhPT09++OEHIiMjAbj55puJiIggIiKCpk2bsnXrVkpKSkhOTqZPnz707duXr7/+usq2MzMz6devn/39DTfcQEBAQJXPffzxx/bpL0aPHm2/K3rFihXcfffd1bbfeeedrF69uso2s7KyCAsLIyIigptuugmA06dPM3LkSEJDQxk3bpz9L4+kpCR27txJREQEd955J4WFhURERNin8U1MTOS9996r7T+tOKN6uQ1MxGQnT540unbtaoSHhxv/9V//ZVgsFsMwDGPfvn1G3759K312+fLlxqBBg4yysjLjiSeeMJYtW2YYhmHs2bPnglP5Pvvss8bs2bOrtF9zzTWV3gcEBNhff/fdd8agQYMMwzCMbt26GcePHzcMwzB+/vlno1OnToZhGMbNN99sbN682b5OYGCgUV5eXmmbSUlJxhdffGEYhmGcOHHCMIyK6ZYff/xxwzAMIyMjw/j1v+nv+/r7+nJzc40777yzSj9Efk9H/HJZ8Pb2ZsuWLbz00ktYrVb69evHt99+W+Vz+/fv5+mnn2bBggW4urry+eef8+yzzxIREcFtt912wYnMCgoKaN26db3UWdspsKOionjqqad4/fXXsVqtQMWw1q9TSyclJXHVVVc5tC1/f38KCgpqV7A4JaeYpE2uDE2aNCExMZHExETKy8tZvXo1t99+u325zWZj1KhRTJ8+nWuuuQaoeCLXypUrLzh086umTZvaQ7c6Xl5elJSU0KJFCw4dOkTbtm0BCAgI4NChQ/j6+vLjjz9WaY+IiODs2bM0adKkyhfDM888Q2JiIitWrKBPnz588803GJc4i4rVarXP6S5SHR3xy2UhLy+Pffv2ARUzGebl5dG+fftKn3niiSe4+eabGTBggL0tPj7efrIVKmYt/b0uXbqwd+/eGmtITExk4cKFACxcuJBbbrkFqDi3MH/+/Grb3333XRISEqpsc+/evURERPCPf/yDDh06cPDgQW666SaWLFkCVJwnuNjUwy4uLpSXl9vf7969+4qejVTqj4JfLgunTp1i1KhRhIaGEh4eTkhICHfccUelz0ydOpV3333XfoI3Ly+PCRMmcOjQIcLDw+nWrRsLFiyosu0hQ4awbt06+/v//u//pl27dhw9epR27drx5ptvAvD3v/+d+fPnExQUhNVqtf+1kZaWhsViISgoiPXr1zNu3DgAhg8fTrNmzejUqRNvvPEGzz77bJV9p6en2/sUGhpKjx49GDduHLt376Znz56sXbvW/tfL740ePZru3bvbT+5mZ2czZMiQS/jXFWej2TlFqBhL/7//+z+uvvrqxi6lijZt2tQ4dl9aWsqAAQNYs2aN0zyBTC6dgl+EiiEgm81G7969G7uUKhwJ/v3797Nnzx4GDhzYQFXJ5UzBLyLiZDTGLyLiZBT8IiJORsEvIuJkFPwiIk5GwS8i4mQU/CIiTub/AdtOrR2H7MLzAAAAAElFTkSuQmCC\n", + "image/png": 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", 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" ] @@ -265,11 +266,11 @@ "# Set the x-axis label\n", "plt.xlabel('Size (1000 sqft)')\n", "plt.show()" - ], - "id": "c1b08142e0243c78" + ] }, { "cell_type": "markdown", + "id": "3d4f63fe1b74df05", "metadata": {}, "source": [ "## Model function\n", @@ -283,12 +284,12 @@ "Let's try to get a better intuition for this through the code blocks below. Let's start with $w = 100$ and $b = 100$. \n", "\n", "**Note: You can come back to this cell to adjust the model's w and b parameters**" - ], - "id": "3d4f63fe1b74df05" + ] }, { "cell_type": "code", "execution_count": 17, + "id": "6ef1590ead23e422", "metadata": {}, "outputs": [ { @@ -305,11 +306,11 @@ "b = 100\n", "print(f\"w: {w}\")\n", "print(f\"b: {b}\")" - ], - "id": "6ef1590ead23e422" + ] }, { "cell_type": "markdown", + "id": "2c9b74a9dafeb729", "metadata": {}, "source": [ "Now, let's compute the value of $f_{w,b}(x^{(i)})$ for your two data points. You can explicitly write this out for each data point as - \n", @@ -321,12 +322,12 @@ "For a large number of data points, this can get unwieldy and repetitive. So instead, you can calculate the function output in a `for` loop as shown in the `compute_model_output` function below.\n", "> **Note**: The argument description `(ndarray (m,))` describes a Numpy n-dimensional array of shape (m,). `(scalar)` describes an argument without dimensions, just a magnitude. \n", "> **Note**: `np.zero(n)` will return a one-dimensional numpy array with $n$ entries \n" - ], - "id": "2c9b74a9dafeb729" + ] }, { "cell_type": "code", "execution_count": 12, + "id": "b6247cda89575683", "metadata": {}, "outputs": [], "source": [ @@ -345,25 +346,25 @@ " f_wb[i] = w * x[i] + b\n", " \n", " return f_wb" - ], - "id": "b6247cda89575683" + ] }, { "cell_type": "markdown", + "id": "ab0afd05a817d94f", "metadata": {}, "source": [ "Now let's call the `compute_model_output` function and plot the output.." - ], - "id": "ab0afd05a817d94f" + ] }, { "cell_type": "code", "execution_count": 18, + "id": "95d34926475c6344", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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"text/plain": [ "
" ] @@ -389,11 +390,11 @@ "plt.xlabel('Size (1000 sqft)')\n", "plt.legend()\n", "plt.show()" - ], - "id": "95d34926475c6344" + ] }, { "cell_type": "markdown", + "id": "a1b84ae24243cb1b", "metadata": {}, "source": [ "As you can see, setting $w = 100$ and $b = 100$ does *not* result in a line that fits our data. \n", @@ -403,11 +404,11 @@ "\n", "#### Tip:\n", "You can use your mouse to click on the green \"Hints\" below to reveal some hints for choosing b and w." - ], - "id": "a1b84ae24243cb1b" + ] }, { "cell_type": "markdown", + "id": "ee76a723f8f5dbd5", "metadata": {}, "source": [ "
\n", @@ -419,21 +420,21 @@ "
  • Try $w = 200$ and $b = 100$
    • \n", " \n", "

    " - ], - "id": "ee76a723f8f5dbd5" + ] }, { "cell_type": "markdown", + "id": "7f423cd19a7ba591", "metadata": {}, "source": [ "### Prediction\n", "Now that we have a model, we can use it to make our original prediction. Let's predict the price of a house with 1200 sqft. Since the units of $x$ are in 1000's of sqft, $x$ is 1.2.\n" - ], - "id": "7f423cd19a7ba591" + ] }, { "cell_type": "code", "execution_count": 14, + "id": "9cdc794cbcf34c22", "metadata": {}, "outputs": [ { @@ -451,11 +452,11 @@ "cost_1200sqft = w * x_i + b \n", "\n", "print(f\"${cost_1200sqft:.0f} thousand dollars\")" - ], - "id": "9cdc794cbcf34c22" + ] }, { "cell_type": "markdown", + "id": "4c8ad73f0d6f18f2", "metadata": {}, "source": [ "# Congratulations!\n", @@ -464,16 +465,15 @@ " - In the example above, the feature was house size and the target was house price\n", " - for simple linear regression, the model has two parameters $w$ and $b$ whose values are 'fit' using *training data*.\n", " - once a model's parameters have been determined, the model can be used to make predictions on novel data." - ], - "id": "4c8ad73f0d6f18f2" + ] }, { "cell_type": "code", "execution_count": null, + "id": "b3eb2771a91f081b", "metadata": {}, "outputs": [], - "source": [], - "id": "b3eb2771a91f081b" + 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