def estimate_components_meng(d):
"""
Compute components using the method of Meng.
"""
U, _, _ = pca_components_gf(d)
C = extract_sparse_components(U, SPCA_SPARSITY, NUM_COMPONENTS, U)
return C
def estimate_components_meng(d):
"""
Compute components using the method of Meng.
"""
U, _, _ = pca_components_gf(d)
C = extract_sparse_components(U, SPCA_SPARSITY, NUM_COMPONENTS, U)
return C
# generate N 10D vectors
X = np.zeros(shape=(N, 10))
v_sel = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2]
for i in range(10):
X[:, i] = V[:, v_sel[i]] + np.random.normal(size=(N, ), scale=sigma)
return X
if __name__ == '__main__':
# program the prolific test example used by everybody
X = make_test_data1(1000, 1.0)
C = spca_meng.extract_sparse_components(X.T, 3, 3)
PC, _, _ = component_analysis.pca_components(X.T)
PC = PC[:, :3]
UR, _, _ = component_analysis.orthomax(PC)
print C
plt.figure()
plt.subplot(311)
plt.plot(C)
plt.title('Sparse PCA')
plt.subplot(312)
plt.plot(PC)
plt.title('PCA components')
plt.subplot(313)
plt.plot(UR)
# generate N 10D vectors
X = np.zeros(shape=(N,10))
v_sel = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2]
for i in range(10):
X[:,i] = V[:,v_sel[i]] + np.random.normal(size = (N,), scale = sigma)
return X
if __name__ == '__main__':
# program the prolific test example used by everybody
X = make_test_data1(1000, 1.0)
C = spca_meng.extract_sparse_components(X.T, 3, 3)
PC,_,_ = component_analysis.pca_components(X.T)
PC = PC[:,:3]
UR, _, _ = component_analysis.orthomax(PC)
print C
plt.figure()
plt.subplot(311)
plt.plot(C)
plt.title('Sparse PCA')
plt.subplot(312)
plt.plot(PC)
plt.title('PCA components')
plt.subplot(313)
plt.plot(UR)
