update

lesson04-简单回归案例-PyTorch求解/data.csv → lesson04-简单回归案例实战/data.csv

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lesson04-简单回归案例-PyTorch求解/gd.py → lesson04-简单回归案例实战/gd.py

@@ -1,50 +1,50 @@
-import numpy as np
-
-# y = wx + b
-def compute_error_for_line_given_points(b, w, points):
-    totalError = 0
-    for i in range(0, len(points)):
-        x = points[i, 0]
-        y = points[i, 1]
-        totalError += (y - (w * x + b)) ** 2
-    return totalError / float(len(points))
-
-def step_gradient(b_current, w_current, points, learningRate):
-    b_gradient = 0
-    w_gradient = 0
-    N = float(len(points))
-    for i in range(0, 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_m = w_current - (learningRate * w_gradient)
-    return [new_b, new_m]
-
-def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations):
-    b = starting_b
-    m = starting_m
-    for i in range(num_iterations):
-        b, m = step_gradient(b, m, np.array(points), learning_rate)
-    return [b, m]
-
-def run():
-    points = np.genfromtxt("data.csv", delimiter=",")
-    learning_rate = 0.0001
-    initial_b = 0 # initial y-intercept guess
-    initial_m = 0 # initial slope guess
-    num_iterations = 1000
-    print("Starting gradient descent at b = {0}, m = {1}, error = {2}"
-          .format(initial_b, initial_m,
-                  compute_error_for_line_given_points(initial_b, initial_m, points))
-          )
-    print("Running...")
-    [b, m] = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations)
-    print("After {0} iterations b = {1}, m = {2}, error = {3}".
-          format(num_iterations, b, m,
-                 compute_error_for_line_given_points(b, m, points))
-          )
-
-if __name__ == '__main__':
+import numpy as np
+
+# y = wx + b
+def compute_error_for_line_given_points(b, w, points):
+    totalError = 0
+    for i in range(0, len(points)):
+        x = points[i, 0]
+        y = points[i, 1]
+        totalError += (y - (w * x + b)) ** 2
+    return totalError / float(len(points))
+
+def step_gradient(b_current, w_current, points, learningRate):
+    b_gradient = 0
+    w_gradient = 0
+    N = float(len(points))
+    for i in range(0, 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_m = w_current - (learningRate * w_gradient)
+    return [new_b, new_m]
+
+def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations):
+    b = starting_b
+    m = starting_m
+    for i in range(num_iterations):
+        b, m = step_gradient(b, m, np.array(points), learning_rate)
+    return [b, m]
+
+def run():
+    points = np.genfromtxt("data.csv", delimiter=",")
+    learning_rate = 0.0001
+    initial_b = 0 # initial y-intercept guess
+    initial_m = 0 # initial slope guess
+    num_iterations = 1000
+    print("Starting gradient descent at b = {0}, m = {1}, error = {2}"
+          .format(initial_b, initial_m,
+                  compute_error_for_line_given_points(initial_b, initial_m, points))
+          )
+    print("Running...")
+    [b, m] = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations)
+    print("After {0} iterations b = {1}, m = {2}, error = {3}".
+          format(num_iterations, b, m,
+                 compute_error_for_line_given_points(b, m, points))
+          )
+
+if __name__ == '__main__':
     run()