def generate_toy(num_x): # get the x features df = DataFrame(np.random.randn(cts.num_examples, num_x), columns=util.get_feature_names(cts.x, num_x)) df = concat([df, df.apply(lambda row: Series(util.get_labels(row)), axis=1)], axis=1) return df
def plot_all(w_learned, data_df): for i in range(len(cts.w_rules[0, :])): (x1_line_rule, x2_line_rule) = get_plot_data(cts.w_rules[:, i], data_df) (x1_line_learned, x2_line_learned) = get_plot_data(w_learned[:, i], data_df) plt.plot(x1_line_rule, x2_line_rule, 'k-', x1_line_learned, x2_line_learned, 'r-') n = int(math.pow(2, cts.num_c)) for i in range(n): binary_string = get_binary_string(i) condition = True for index, name in enumerate(util.get_feature_names('c', cts.num_c)): condition &= (data_df[name] == int(binary_string[index])) my_filter = data_df[condition] plt.plot(my_filter[cts.x1], my_filter[cts.x2], 's', color=colorsys.hsv_to_rgb(*(i*1.0/n, 0.8, 0.8))) plt.show()
def get_feature_names(): response=jsonify({'features': util.get_feature_names()}) response.headers.add('Access-Control-Allow-Origin', '*') return response