241005

linear regression/1.md

@@ -1,18 +0,0 @@
-# 1.py
-
-$$
-y = wx + b
-$$
-
-## 梯度下降算法
-
-$$
-b_gradient += -\frac{2}{N} \left(y - (w_current \cdot x + b_current)\right)
-$$
-
-
-
-$$
-w_gradient += -\frac{2}{N} \cdot x \cdot \left(y - (w_current \cdot x + b_current)\right)
-$$
-

linear regression/1.py

@@ -1,7 +1,12 @@
 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))
@@ -11,7 +16,6 @@ def compute_error_for_line_given_points(b, w, points):
             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)
@@ -25,7 +29,6 @@ def step_gradient(b_current, w_current, points, learningRate):
         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)
@@ -33,7 +36,6 @@ def gradient_descent_runner(points, starting_b, starting_w, learningRate, num_it
             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)
@@ -49,8 +51,260 @@ def run():
         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)))
+                                                                       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__':
-    run()
+    print("start")

linear regression/1_cuda.py

@@ -1,73 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-
-# 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)
-    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()

linear regression/1x.py

@@ -1,72 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-
-# 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()

linear regression/m1.py

@@ -1,76 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.utils.data import DataLoader, TensorDataset
-import matplotlib.pyplot as plt
-
-
-# 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()

linear regression/m1test.py

@@ -1,56 +0,0 @@
-import torch
-import torch.nn as nn
-import matplotlib.pyplot as plt
-
-
-# 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()
-
-
-if __name__ == "__main__":
-    main()