def test_LinearRegression_multivariate():
"""Tests probflow.applications.LinearRegression w/ d_o>1"""
# Data
N = 256
Di = 7
Do = 3
x = np.random.randn(N, Di).astype("float32")
w = np.random.randn(Di, Do).astype("float32")
y = x @ w + 0.1 * np.random.randn(N, Do).astype("float32")
# Create the model
model = apps.LinearRegression(Di, d_o=Do)
# Fit the model
model.fit(x, y, batch_size=128, epochs=3)
# Predictive functions
preds = model.predict(x[:11, :])
assert isinstance(preds, np.ndarray)
assert preds.ndim == 2
assert preds.shape[0] == 11
assert preds.shape[1] == Do
# Predictive functions
preds = model.predictive_sample(x[:11, :], n=13)
assert isinstance(preds, np.ndarray)
assert preds.ndim == 3
assert preds.shape[0] == 13
assert preds.shape[1] == 11
assert preds.shape[2] == Do
def test_LinearRegression_heteroscedastic():
"""Tests probflow.applications.LinearRegression w/ heteroscedastic"""
# Data
x = np.random.randn(100, 5).astype("float32")
w = np.random.randn(5, 1).astype("float32")
y = x @ w + 1
y = y + np.exp(y) * np.random.randn(100, 1).astype("float32")
# Create the model
model = apps.LinearRegression(5, heteroscedastic=True)
# Fit the model
model.fit(x, y, batch_size=10, epochs=3)
# Predictive functions
preds = model.predict(x[:11, :])
assert isinstance(preds, np.ndarray)
assert preds.ndim == 2
assert preds.shape[0] == 11
assert preds.shape[1] == 1
# predictive interval
lb, ub = model.predictive_interval(x[:12, :], ci=0.9)
assert isinstance(lb, np.ndarray)
assert lb.ndim == 2
assert lb.shape[0] == 12
assert lb.shape[1] == 1
assert isinstance(ub, np.ndarray)
assert ub.ndim == 2
assert ub.shape[0] == 12
assert ub.shape[1] == 1
def test_LinearRegression():
"""Tests probflow.applications.LinearRegression"""
# Data
x = np.random.randn(100, 5).astype('float32')
w = np.random.randn(5, 1).astype('float32')
y = x @ w + 1
# Create the model
model = apps.LinearRegression(5)
# Fit the model
model.fit(x, y, batch_size=10, epochs=11)
# Predictive functions
model.predict(x)
def test_LinearRegression_heteroscedastic():
"""Tests probflow.applications.LinearRegression w/ heteroscedastic"""
# Data
x = np.random.randn(100, 5).astype("float32")
w = np.random.randn(5, 1).astype("float32")
y = x @ w + 1
y = y + np.exp(y) * np.random.randn(100, 1).astype("float32")
# Create the model
model = apps.LinearRegression(5, heteroscedastic=True)
# Fit the model
model.fit(x, y, batch_size=10, epochs=3)
# Predictive functions
model.predict(x)