48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
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def generate_toy_data(n_atoms, n_samples, image_size):
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n_features = image_size[0] * image_size[1]
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np.random.seed(0)
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U = np.random.randn(n_samples, n_atoms)
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V = np.random.randn(n_atoms, n_features)
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centers = [(3, 3), (6, 7), (8, 1)]
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sz = [1, 2, 1]
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for k in range(n_atoms):
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img = np.zeros(image_size)
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xmin, xmax = centers[k][0] - sz[k], centers[k][0] + sz[k]
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ymin, ymax = centers[k][1] - sz[k], centers[k][1] + sz[k]
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img[xmin:xmax][:, ymin:ymax] = 1.0
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V[k, :] = img.ravel()
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# Y is defined by : Y = UV + noise
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Y = np.dot(U, V)
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Y += 0.1 * np.random.randn(Y.shape[0], Y.shape[1]) # Add noise
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return Y, U, V
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# Generate toy data
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n_atoms = 3
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n_samples = 100
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img_sz = (10, 10)
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Y, U, V = generate_toy_data(n_atoms, n_samples, img_sz)
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# Estimate U,V
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alpha = 0.5
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SPCA = SparsePCA(n_atoms, alpha, max_iter=100, method='lasso', n_jobs=1)
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SPCA.fit(Y)
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V_estimated = SPCA.components_
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# View results
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import pylab as pl
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pl.close('all')
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for k in range(n_atoms):
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pl.matshow(np.reshape(V_estimated[k, :], img_sz))
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pl.title('Atom %d' % k)
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pl.colorbar()
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pl.figure()
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pl.plot(SPCA.error_)
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pl.xlabel('Iteration')
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pl.ylabel('Cost function')
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pl.show()
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