first commit

visual.py

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+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import numpy as np
+
+
+def plot_dataset(dataset):
+	x0 = [dot.x for dot in dataset.train if not dot.classification]
+	y0 = [dot.y for dot in dataset.train if not dot.classification]
+	x1 = [dot.x for dot in dataset.train if dot.classification]
+	y1 = [dot.y for dot in dataset.train if dot.classification]
+
+	plt.scatter(x0, y0, color='green', label='Class 0')
+	plt.scatter(x1, y1, color='red', label='Class 1')
+    
+def plot_decision_surface(network, resolution=0.02):
+	x_min, x_max = -1, 1
+	y_min, y_max = -1, 1
+	xx, yy = np.meshgrid(np.arange(x_min, x_max, resolution),
+							np.arange(y_min, y_max, resolution))
+
+	# прогоняем сетку через сеть
+	Z = np.array([network.predict([x, y]) for x, y in zip(xx.ravel(), yy.ravel())])
+	Z = Z.reshape(xx.shape)
+
+	# закрашиваем фон по вероятности
+	plt.contourf(xx, yy, Z, levels=50, cmap='RdYlGn', alpha=0.3)
+    
+def plot_all(dataset, network):
+	plt.figure(figsize=(6,6))
+	plot_decision_surface(network)
+	plot_dataset(dataset)
+	plt.xlim(-1, 1)
+	plt.ylim(-1, 1)
+	plt.legend()
+
+def plt_show():
+	plt.show()
+
+
+if __name__ == "__main__":
+	pass