def test_concatenate(app_inst: ArrayApplication):
axis = 1
real_X, _ = BimodalGaussian.get_dataset(1000, 9)
real_ones = np.ones(shape=(1000, 1))
X = app_inst.array(real_X, block_shape=(100, 9))
ones = app_inst.ones((1000, 1), (100, 1), dtype=X.dtype)
X_concated = app_inst.concatenate([X, ones],
axis=axis,
axis_block_size=X.block_shape[axis])
common.check_block_integrity(X_concated)
real_X_concated = np.concatenate([real_X, real_ones], axis=axis)
assert np.allclose(X_concated.get(), real_X_concated)
real_X2 = np.random.random_sample(1000 * 17).reshape(1000, 17)
X2 = app_inst.array(real_X2, block_shape=(X.block_shape[0], 3))
X_concated = app_inst.concatenate([X, ones, X2],
axis=axis,
axis_block_size=X.block_shape[axis])
common.check_block_integrity(X_concated)
real_X_concated = np.concatenate([real_X, real_ones, real_X2], axis=axis)
assert np.allclose(X_concated.get(), real_X_concated)
y1 = app_inst.zeros(shape=(50, ), block_shape=(10, ), dtype=int)
y2 = app_inst.ones(shape=(50, ), block_shape=(10, ), dtype=int)
y = app_inst.concatenate([y1, y2], axis=0)
common.check_block_integrity(y)
def sample(app: ArrayApplication, sample_size, feature, num_gpus, augment=False):
if num_gpus == 2:
# X_train = nps.concatenate([app.random.normal(shape=(sample_size // 2, feature), block_shape=(sample_size // 2, feature), dtype=np.float64),
# app.random.normal(shape=(sample_size // 2, feature), block_shape=(sample_size // 2, feature), dtype=np.float64) + 2.0],
# axis=0)
# y_train = nps.concatenate([app.zeros(shape=(sample_size // 2,),
# block_shape=(sample_size // 2,),
# dtype=nps.int),
# app.ones(shape=(sample_size // 2,),
# block_shape=(sample_size // 2,),
# dtype=nps.int)], axis=0)
print("X_train")
X_train = app.random.normal(shape=(sample_size, feature), block_shape=(sample_size // 2, feature),
dtype=np.float64)
print("y_train")
y_train = app.ones(shape=(sample_size, 1), block_shape=(sample_size // 2, 1), dtype=nps.int)
else:
assert num_gpus is 1
# X_train = app.random.normal(shape=(sample_size, feature), block_shape=(sample_size, feature), dtype=np.float64)
# y_train = app.ones(shape=(sample_size,), block_shape=(sample_size,),
# dtype=nps.int)
X_train = app.random.normal(shape=(sample_size, feature), block_shape=(sample_size, feature), dtype=np.float64)
y_train = app.ones(shape=(sample_size, 1), block_shape=(sample_size, 1), dtype=nps.int)
# We augment X with 1s for intercept term.
if augment:
X_train = app.concatenate([X_train, app.ones(shape=(X_train.shape[0], 1),
block_shape=(X_train.block_shape[0], 1),
dtype=X_train.dtype)],
axis=1,
axis_block_size=X_train.block_shape[1] + 1)
return X_train, y_train
def sample_set(app: ArrayApplication):
shape = (500, 10)
block_shape = (100, 10)
rs = app.random_state(1337)
X1 = rs.normal(loc=5.0, shape=shape, block_shape=block_shape)
y1 = app.zeros(shape=(shape[0],), block_shape=(block_shape[0],), dtype=int)
X2 = rs.normal(loc=10.0, shape=shape, block_shape=block_shape)
y2 = app.ones(shape=(shape[0],), block_shape=(block_shape[0],), dtype=int)
X = app.concatenate([X1, X2], axis=0)
y = app.concatenate([y1, y2], axis=0)
return X, y
def sample(app: ArrayApplication, sample_size):
X_train = nps.concatenate([
nps.random.randn(sample_size // 2, 2),
nps.random.randn(sample_size // 2, 2) + 2.0
],
axis=0)
y_train = nps.concatenate([
nps.zeros(shape=(sample_size // 2, ), dtype=nps.int),
nps.ones(shape=(sample_size // 2, ), dtype=nps.int)
],
axis=0)
# We augment X with 1s for intercept term.
X_train = app.concatenate([
X_train,
app.ones(shape=(X_train.shape[0], 1),
block_shape=(X_train.block_shape[0], 1),
dtype=X_train.dtype)
],
axis=1,
axis_block_size=X_train.block_shape[1] + 1)
return X_train, y_train
