def test_array_to_scalar():
a = array_to_scalar(np.array(1))
assert isinstance(a, int)
assert a == 1
b = array_to_scalar(np.array(1.5))
assert isinstance(b, float)
assert b == 1.5
def test_transpose2(x, y, axis1, axis2):
perm = (scalar_cast(axis1, u64), scalar_cast(axis2, u64))
xt = transpose(x, perm)
yt = transpose(y, perm)
d = dot(xt, yt)
sm = array_reduce(scalar_add, d, ())
return array_to_scalar(sm)
def prod(xs):
p = array_reduce(lambda x, y: x * y, xs, ())
return array_to_scalar(p)
def test_array_to_scalar(x):
return array_to_scalar(x)
def cost(model, x, target):
y = model.apply(x)
diff = target - y
diffsqr = diff * diff
return array_to_scalar(array_reduce(scalar_add, diff * diff, ()))
def test_transpose(x, y):
xt = transpose(x, (1, 0))
yt = transpose(y, (1, 0))
d = dot(xt, yt)
sm = array_reduce(scalar_add, d, ())
return array_to_scalar(sm)
def test_dot(x, y):
d = dot(x, y)
sm = array_reduce(scalar_add, d, ())
return array_to_scalar(sm)
def test_array_operations_std(xs, ys):
div = xs / ys
sm = array_reduce(scalar_add, div, ())
return array_to_scalar(sm)
def test_array_operations_reshape(xs, ys):
xs = reshape(xs, (6, ))
ys = reshape(ys, (6, ))
div = array_map(scalar_div, xs, ys)
sm = array_reduce(scalar_add, div, ())
return array_to_scalar(sm)
def test_array_operations_distribute(x, y):
xs = distribute(scalar_to_array(x), (4, 3))
ys = distribute(scalar_to_array(y), (4, 3))
div = array_map(scalar_div, xs, ys)
sm = array_reduce(scalar_add, div, ())
return array_to_scalar(sm)
def test_array_operations(xs, ys):
div = array_map(scalar_div, xs, ys)
sm = array_reduce(scalar_add, div, ())
return array_to_scalar(sm)
