removing invalid character on windows

python/Supervised Learning/Linear Regression/16 Quiz Mini-Batch Gradient Descent/batch_graddesc.py

@@ -0,0 +1,92 @@
+import numpy as np
+# Setting a random seed, feel free to change it and see different solutions.
+np.random.seed(42)
+
+
+# TODO: Fill in code in the function below to implement a gradient descent
+# step for linear regression, following a squared error rule. See the docstring
+# for parameters and returned variables.
+def MSEStep(X, y, W, b, learn_rate=0.005):
+    """
+    This function implements the gradient descent step for squared error as a
+    performance metric.
+
+    Parameters
+    X : array of predictor features
+    y : array of outcome values
+    W : predictor feature coefficients
+    b : regression function intercept
+    learn_rate : learning rate
+
+    Returns
+    W_new : predictor feature coefficients following gradient descent step
+    b_new : intercept following gradient descent step
+    """
+
+    # compute errors
+    y_pred = np.matmul(X, W) + b
+    error = y - y_pred
+
+    # compute steps
+    W_new = W + learn_rate * np.matmul(error, X)
+    b_new = b + learn_rate * error.sum()
+    return W_new, b_new
+
+    return W_new, b_new
+
+
+# The parts of the script below will be run when you press the "Test Run"
+# button. The gradient descent step will be performed multiple times on
+# the provided dataset, and the returned list of regression coefficients
+# will be plotted.
+def miniBatchGD(X, y, batch_size=20, learn_rate=0.005, num_iter=25):
+    """
+    This function performs mini-batch gradient descent on a given dataset.
+
+    Parameters
+    X : array of predictor features
+    y : array of outcome values
+    batch_size : how many data points will be sampled for each iteration
+    learn_rate : learning rate
+    num_iter : number of batches used
+
+    Returns
+    regression_coef : array of slopes and intercepts generated by gradient
+      descent procedure
+    """
+    n_points = X.shape[0]
+    W = np.zeros(X.shape[1])  # coefficients
+    b = 0  # intercept
+
+    # run iterations
+    regression_coef = [np.hstack((W, b))]
+    for _ in range(num_iter):
+        batch = np.random.choice(range(n_points), batch_size)
+        X_batch = X[batch, :]
+        y_batch = y[batch]
+        W, b = MSEStep(X_batch, y_batch, W, b, learn_rate)
+        regression_coef.append(np.hstack((W, b)))
+
+    return regression_coef
+
+
+if __name__ == "__main__":
+    # perform gradient descent
+    data = np.loadtxt('data.csv', delimiter=',')
+    X = data[:, :-1]
+    y = data[:, -1]
+    regression_coef = miniBatchGD(X, y)
+
+    # plot the results
+    import matplotlib.pyplot as plt
+
+    plt.figure()
+    X_min = X.min()
+    X_max = X.max()
+    counter = len(regression_coef)
+    for W, b in regression_coef:
+        counter -= 1
+        color = [1 - 0.92 ** counter for _ in range(3)]
+        plt.plot([X_min, X_max], [X_min * W + b, X_max * W + b], color=color)
+    plt.scatter(X, y, zorder=3)
+    plt.show()