def test_pca(self):
ctx = blob_ctx.get()
A = expr.randn(*DIM, tile_hint=(int(DIM[0]/ctx.num_workers), DIM[1])).force()
m = PCA(N_COMPONENTS)
m2 = SK_PCA(N_COMPONENTS)
m.fit(A)
m2.fit(A.glom())
assert np.allclose(absolute(m.components_), absolute(m2.components_))
def benchmark_pca(ctx, timer):
DIM = (1280, 512)
data = np.random.randn(*DIM)
A = expr.from_numpy(data)
#A = expr.randn(*DIM, dtype=np.float64)
t1 = datetime.now()
m = PCA(N_COMPONENTS)
m.fit(A)
t2 = datetime.now()
cost_time = millis(t1, t2)
print "total cost time:%s ms" % (cost_time)
def test_pca(self):
FLAGS.opt_parakeet_gen = 0
data = np.random.randn(*DIM)
A = expr.from_numpy(data, tile_hint=util.calc_tile_hint(DIM, axis=0))
m = PCA(N_COMPONENTS)
m2 = SK_PCA(N_COMPONENTS)
m.fit(A)
m2.fit(data)
print m2.components_ - m.components_
assert np.allclose(absolute(m.components_), absolute(m2.components_))
def benchmark_pca(ctx, timer):
DIM = (1280, 512)
data = np.random.randn(*DIM)
A = expr.from_numpy(data)
#A = expr.randn(*DIM, dtype=np.float64)
t1 = datetime.now()
m = PCA(N_COMPONENTS)
m.fit(A)
t2 = datetime.now()
cost_time = millis(t1, t2)
print "total cost time:%s ms" % (cost_time)
def test_pca(self):
FLAGS.opt_parakeet_gen = 0
data = np.random.randn(*DIM)
A = expr.from_numpy(data, tile_hint=util.calc_tile_hint(DIM, axis=0))
m = PCA(N_COMPONENTS)
m2 = SK_PCA(N_COMPONENTS)
m.fit(A)
m2.fit(data)
print m2.components_ - m.components_
assert np.allclose(absolute(m.components_), absolute(m2.components_))