def test():
anp = np.random.randn(4, 5)
b = random.randint(1, 13)
a = lg.array(anp)
np.power(anp, b, out=anp)
lg.power(a, b, out=a)
assert lg.allclose(a, anp)
return
def test():
anp = np.random.randn(4, 5)
b = random.randint(1, 13)
a = lg.array(anp)
# test power with scalar on rhs
assert lg.allclose(lg.power(a, b), np.power(anp, b))
# test power with scalar on lhs
assert lg.allclose(lg.power(b, a), np.power(b, anp))
return
def test():
bases_np = np.random.randn(4, 5)
# avoid fractional exponents
exponents_np = np.random.randint(10, size=(4, 5)).astype(np.float64)
bases = lg.array(bases_np)
exponents = lg.array(exponents_np)
np.power(bases_np, exponents_np, out=bases_np)
lg.power(bases, exponents, out=bases)
assert lg.allclose(bases, bases_np)
def test():
random.seed(13)
a = random.randint(1, 42)
b = random.randint(1, 3)
# test power
assert np.array_equal(lg.power(a, b), np.power(a, b))
return
def linear_regression(T,
features,
target,
steps,
learning_rate,
sample,
add_intercept=False):
if add_intercept:
intercept = np.ones((features.shape[0], 1), dtype=T)
features = np.hstack((intercept, features))
weights = np.zeros(features.shape[1], dtype=T)
for step in range(steps):
scores = np.dot(features, weights)
error = scores - target
gradient = -(1.0 / len(features)) * error.dot(features)
weights += learning_rate * gradient
if step % sample == 0:
print("Error of step " + str(step) + ": " +
str(np.sum(np.power(error, 2))))
return weights