def uniform_value_parameter_sweep(actual,title=None): errors = [] fixed_views_value = np.arange(0.0,10.0,0.1) for i in fixed_views_value: predicted_num_views = uniform_array(i,num_points) error = cost_function.error_function(predicted_num_views,actual) errors.append(error) plt.plot(fixed_views_value,errors) plt.title = title plt.show()
error = cost_function.error_function(predicted_num_views,actual) errors.append(error) plt.plot(fixed_views_value,errors) plt.title = title plt.show() #uniform_value_parameter_sweep(d.num_views, title = 'param sweep for num_views') #uniform_value_parameter_sweep(d.num_comments, title = 'param sweep for num_comments') #uniform_value_parameter_sweep(d.num_votes, title = 'param sweep for num_votes') mean_num_views = uniform_array(1.8,num_points) mean_num_votes = uniform_array(1.3,num_points) mean_num_comments = uniform_array(0.0,num_points) mean_num_views_error = cost_function.error_function(mean_num_views,d.num_views) mean_num_votes_error = cost_function.error_function(mean_num_votes,d.num_votes) mean_num_comments_error = cost_function.error_function(mean_num_comments,d.num_comments) zeros = np.zeros(num_points) zeros_num_views_error = cost_function.error_function(zeros,d.num_views) zeros_num_comments_error = cost_function.error_function(zeros,d.num_comments) zeros_num_votes_error = cost_function.error_function(zeros,d.num_votes) print("Mean num views error:") print mean_num_views_error print("Mean num votes error:") print mean_num_votes_error print("Mean num comments error:")