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25 lines
No EOL
634 B
Python
25 lines
No EOL
634 B
Python
#!/usr/bin/env python
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# Adapted from: http://cs231n.github.io/neural-networks-case-study/
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import numpy as np
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import matplotlib.pyplot as plt
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N = 100 # number of points per class
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D = 2 # dimensionality
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K = 3 # number of classes
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# data matrix (each row = single example)
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X = np.zeros((N*K, D))
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# class labels
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y = np.zeros(N*K, dtype='uint8')
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for j in range(K):
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ix = range(N*j,N*(j+1))
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r = np.linspace(0.0,1,N) # radius
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t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
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X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
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y[ix] = j
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# visualize the data:
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plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral) |