def test_tga_median():
# TGA on dense arrays
tga = TGA(n_components=2, random_state=1, centering='median')
X = iris.data
X_r = tga.fit(X).transform(X)
assert_equal(X_r.shape[1], 2)
tga = TGA(n_components=2, random_state=1, centering='median')
X_r2 = tga.fit_transform(X)
assert_array_almost_equal(X_r, X_r2)
tga = TGA(random_state=1, centering='median')
tga.fit(X)
X_r = tga.transform(X)
tga = TGA(random_state=1, centering='median')
X_r2 = tga.fit_transform(X)
assert_array_almost_equal(X_r, X_r2)
ax.set_xticklabels([])
ax.set_yticklabels([])
if __name__ == "__main__":
import sys
import glob
import matplotlib.pyplot as pl
use_data = str(sys.argv[1])
M, shape = bitmap_to_mat(glob.glob(use_data + "/*.bmp")[:2000:2])
print(M.shape)
tga = TGA(n_components=5, random_state=1)
start_time = time.time()
tga.fit(M)
print("fitted, time taken {0}s".format(time.time() - start_time))
start_time = time.time()
transformed = tga.transform(M)
L = tga.inverse_transform(transformed)
print('calculated L, time taken {0}s'.format(time.time() - start_time))
S = M - L
if not os.path.exists('results_tga'):
os.makedirs('results_tga')
directory = "results_tga/" + use_data
if not os.path.exists(directory):
os.makedirs(directory)
fig, axes = pl.subplots(1, 3, figsize=(10, 4))
