adding all work done so far (lessons 1 - 5)

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

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+def MSEStep(X, y, W, b, learn_rate = 0.001):
+    """
+    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