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plots.py
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	# -- coding: utf-8 --
	"""visualize the result."""
	import numpy as np
	import matplotlib.pyplot as plt

	from helpers import de_standardize, standardize


	def visualization(y, x, mean_x, std_x, w, save_name):
	"""visualize the raw data as well as the classification result."""
	fig = plt.figure()
	# plot raw data
	x = de_standardize(x, mean_x, std_x)
	ax1 = fig.add_subplot(1, 2, 1)
	males = np.where(y == 1)
	females = np.where(y == 0)
	ax1.scatter(
	x[males, 0], x[males, 1],
	marker='.', color=[0.06, 0.06, 1], s=20)
	ax1.scatter(
	x[females, 0], x[females, 1],
	marker='*', color=[1, 0.06, 0.06], s=20)
	ax1.set_xlabel("Height")
	ax1.set_ylabel("Weight")
	ax1.grid()
	# plot raw data with decision boundary
	ax2 = fig.add_subplot(1, 2, 2)
	height = np.arange(
	np.min(x[:, 0]), np.max(x[:, 0]) + 0.01, step=0.01)
	weight = np.arange(
	np.min(x[:, 1]), np.max(x[:, 1]) + 1, step=1)
	hx, hy = np.meshgrid(height, weight)
	hxy = (np.c_[hx.reshape(-1), hy.reshape(-1)] - mean_x) / std_x
	x_temp = np.c_[np.ones((hxy.shape[0], 1)), hxy]
	prediction = x_temp.dot(w) > 0.5
	prediction = prediction.reshape((weight.shape[0], height.shape[0]))
	ax2.contourf(hx, hy, prediction, 1)
	ax2.scatter(
	x[males, 0], x[males, 1],
	marker='.', color=[0.06, 0.06, 1], s=20)
	ax2.scatter(
	x[females, 0], x[females, 1],
	marker='*', color=[1, 0.06, 0.06], s=20)
	ax2.set_xlabel("Height")
	ax2.set_ylabel("Weight")
	ax2.set_xlim([min(x[:, 0]), max(x[:, 0])])
	ax2.set_ylim([min(x[:, 1]), max(x[:, 1])])
	plt.tight_layout()
	plt.savefig(save_name)