Clean up this scratch space

numpy_examples/nn_case_study.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python 
+# Adapted from: http://cs231n.github.io/neural-networks-case-study/
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+N = 100 # number of points per class
+D = 2 # dimensionality
+K = 3 # number of classes
+
+# data matrix (each row = single example)
+X = np.zeros((N*K, D)) 
+# class labels
+y = np.zeros(N*K, dtype='uint8') 
+
+for j in range(K):
+  ix = range(N*j,N*(j+1))
+  r = np.linspace(0.0,1,N) # radius
+  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
+  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
+  y[ix] = j
+
+
+# visualize the data:
+plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)