ifnull · February 13, 2021 23:31 · Feb 13, 2021
diff --git a/main.py b/main.py
@@ -0,0 +1,126 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def plot_dataset(x, y, legend_loc='lower left'):
+	fig, ax = plt.subplots()
+
+	ax.scatter(
+		x[y == 1, 0],
+		x[y == 1, 1],
+		c='r',
+		s=100,
+		alpha=0.7,
+		marker='*',
+		label='Sea Bass',
+		linewidth=0)
+
+	ax.scatter(
+		x[y == - 1, 0],
+		x[y == -1, 1],
+		c='b',
+		s=100,
+		alpha=0.7,
+		marker='o',
+		label='Salmon',
+		linewidth=0)
+
+	ax.axhline(
+		y=0,
+		color='k')
+
+	ax.axvline(
+		x=0,
+		color='k')
+
+	ax.set_xlabel('Length')
+	ax.set_ylabel('Lightness') 
+	ax.set_aspect('equal')
+
+	if legend_loc:
+		ax.legend(loc=legend_loc, fancybox=True).get_frame().set_alpha(0.5)
+		ax.grid('on')
+
+	plt.show()
+
+
+# For the three figures in part 1
+x = np.array([
+	[2, 1], 
+	[0, -1], 
+	[1.5, 0], 
+	[0, 1], 
+	[-1, 1], 
+	[-3, 0],
+	[1, -1],
+	[2, - 1],
+	[3, -2],
+	[3, 1],
+	[-2, 1.5],
+	[-3, 0.5],
+	[-1, 2]]) 
+
+y = np.array([1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, - 1, -1]) 
+
+# Show Part 1 - Figure 1
+plot_dataset(x, y)
+
+x2 = np.vstack([
+	x, 
+	np.array([0, -0.2])])
+
+y2 = np.hstack([y, np.array([-1])]) 
+
+# Show Part 1 - Figure 2
+plot_dataset(x2, y2)
+
+x3 = np.array([
+	[4, 1],
+	[-2, 1],
+	[-2, - 4],
+	[-1, -1],
+	[2, -1],
+	[-1, -3],
+	[3, 2],
+	[1, 2.5],
+	[-3, -1],
+	[-3, 3],
+	[0,	-2],
+	[4, -2],
+	[3, -4]])
+
+y3 = np.array([1, 1, 1, -1, - 1, -1, 1, 1, 1, 1, -1, -1, -1])
+
+# Show Part 1 - Figure 3
+plot_dataset(x3, y3, legend_loc='lower right')
+
+# For the sigmoid network in part 2 
+def sigmoid(inputs):
+	return 1.0 / (1.0 + np.exp(-inputs))
+
+
+def nn_2layer(inputs):
+	return np.sign(sigmoid(inputs[:, 0]) + sigmoid(-inputs[:, 1]) - 1.5)
+
+
+def plot_decision_boundary(network):
+	x0v, x1v = np.meshgrid(np.linspace(-2, 8, 20), np.linspace(-8, 2, 20)) 
+	x4 = np.hstack([x0v.reshape((-1, 1)), x1v.reshape((-1, 1))])
+	y4 = network(x4)
+	plot_dataset(x4, y4, legend_loc=None)
+
+
+# Show Part 2 - Figure 1
+plot_decision_boundary(nn_2layer)
+
+# For the ReLU network in Part 2 
+def relu(inputs):
+	return np.maximum(0, inputs)
+
+
+def nn_2layer_relu(inputs):
+	return np.sign(relu(-inputs[:, 0]) + relu(inputs[:, 1]) - 0.1)
+
+
+# Show Part 2 - Figure 2
+plot_decision_boundary(nn_2layer_relu)
diff --git a/requirements.txt b/requirements.txt
@@ -0,0 +1,2 @@
+matplotlib==3.3.4
+numpy==1.18.0