多元线性回归返回相同的预测值

问题描述

我的线性回归中可能有一个错误，因为预测给了我完全相同的值。

我认为错误可能在梯度下降

def gradient_descent(X,y,theta,alpha,iterations,do_plot):

fig,ax1 = plt.subplots()
if do_plot == True:
    plot_hypothesis(X,ax1)

m = X.shape[0]  # the number of training samples is the number of rows of array X
cost_vector = np.array([],dtype=np.float32)  # empty array to store the cost for every iteration

# Gradient Descent loop
for it in range(iterations):

    # initialize temporary theta,as a copy of the existing theta array
    theta_temp = theta.copy()

    sigma = np.zeros((len(theta)))
    for i in range(m):

        hypothesis = calculate_hypothesis(X,i)
        output = y[i]

        sigma = sigma + (hypothesis - output)

        theta_temp = theta_temp - (alpha / m) * sigma


    theta = theta_temp.copy()
    print(m)
    # append current iteration's cost to cost_vector
    iteration_cost = compute_cost(X,theta)

    cost_vector = np.append(cost_vector,iteration_cost)


    if do_plot == True:
        plot_hypothesis(X,ax1)



plot_hypothesis(X,ax1)
# save the predictions as a figure
plot_filename = os.path.join(os.getcwd(),'figures','predictions.png')
plt.savefig(plot_filename)
print('Gradient descent finished.')

# Plot the cost for all iterations
plot_cost(cost_vector)
min_cost = np.min(cost_vector)
argmin_cost = np.argmin(cost_vector)
print('Minimum cost: {:.5f},on iteration #{}'.format(min_cost,argmin_cost + 1))

return theta

最终，我可能弄错了这个假设，该假设应该接受足够数量的值

def calculate_hypothesis(X,i):
"""
    :param X            : 2D array of our dataset
    :param theta        : 1D array of the trainable parameters
    :param i            : scalar,index of current training sample's row
"""

print(X[1])
hypothesis = np.dot(X[1],theta)

return hypothesis

你们中的任何人都能发现错误吗？

只是想像一下，我将向您展示剧情 plot

解决方法

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