def de_mean(A): """ returns result of subtracting from every value of A the mean value of its column """ num_rows, num_cols = Ch4.shape(A) col_means, _ = scale(A) return Ch4.make_matrix(num_rows,num_cols, lambda i,j: A[i][j]-col_means[j])
def de_mean(A): """ returns result of subtracting from every value of A the mean value of its column """ num_rows, num_cols = Ch4.shape(A) col_means, _ = scale(A) return Ch4.make_matrix(num_rows, num_cols, lambda i, j: A[i][j] - col_means[j])
plt.ylabel('actual') # Compute beta_hat by SGD # ########################### # pick a random initial beta (constant, beta1*experience, beta2*salary) beta_0 = [random.random() for _ in range(3)] beta_hat = maximize_stochastic(logistic_log_likelihood_i, logistic_log_gradient_i, x_train, y_train, beta_0) print "beta_hat", beta_hat # Transform beta_hat back to unscaled variables # ################################################# # get the means and stds of const, years, experience cols in data means_x, stds_x = scale(x) # beta_i i!=0 has the following transform beta_i = beta_i_scaled/sigma_i # and beta_0 is beta_hat_unscaled = [ beta_hat[0], beta_hat[1] / stds_x[1], beta_hat[2] / stds_x[2] ] print "beta_hat_unscaled", beta_hat_unscaled # Fit Quality # ############### # Examine the test data true_positives = false_positives = true_negatives = false_negatives = 0 for x_i, y_i in zip(x_test, y_test): # For the test data get a prediction for y. This will be a
plt.ylabel('actual') # Compute beta_hat by SGD # ########################### # pick a random initial beta (constant, beta1*experience, beta2*salary) beta_0 = [random.random() for _ in range(3)] beta_hat = maximize_stochastic(logistic_log_likelihood_i, logistic_log_gradient_i, x_train, y_train, beta_0) print "beta_hat", beta_hat # Transform beta_hat back to unscaled variables # ################################################# # get the means and stds of const, years, experience cols in data means_x, stds_x = scale(x) # beta_i i!=0 has the following transform beta_i = beta_i_scaled/sigma_i # and beta_0 is beta_hat_unscaled =[beta_hat[0], beta_hat[1]/stds_x[1], beta_hat[2]/stds_x[2]] print "beta_hat_unscaled", beta_hat_unscaled # Fit Quality # ############### # Examine the test data true_positives = false_positives = true_negatives = false_negatives = 0 for x_i, y_i in zip(x_test, y_test): # For the test data get a prediction for y. This will be a