def test_kmeans():
n = 200
d = 4
X = np.random.randn(n*d).reshape(n,d)
k = 2
niters = 10
assignments = np.random.randint(0, k, size = n)
parakeet_C = parakeet_kmeans(X, k, niters, assignments)
python_C = python_kmeans(X, k, niters, assignments)
assert eq(parakeet_C.shape, python_C.shape), \
"Got back wrong shape, expect %s but got %s" % \
(python_C.shape, parakeet_C.shape)
assert eq(parakeet_C, python_C), \
"Expected %s but got %s" % (python_C, parakeet_C)
def test_set_1d_step_slice_to_const():
x = np.array([1,2,3,4,5,6])
x2 = x.copy()
idx = slice(2,4,2)
y = 0
x[idx] = y
setidx(x2, idx, y)
assert testing_helpers.eq(x, x2)
def test_set_1d_negative_slice():
x = np.array([1,2,3,4,5,6])
x2 = x.copy()
idx = slice(4,2,-1)
y = [10, 20]
x[idx] = y
setidx(x2, idx, y)
assert testing_helpers.eq(x, x2), "Expected %s but got %s" % (x, x2)
def test_sqr_dist():
z = a[0]
def run_sqr_dist(c):
return sqr_dist(z, c)
par_rslt = parakeet.each(run_sqr_dist, a)
py_rslt = np.array(map(run_sqr_dist, a))
assert testing_helpers.eq(par_rslt, py_rslt), \
"Expected %s but got %s" % (py_rslt, par_rslt)
def test_black_scholes():
x1 = (False, 10.0, 10.0, 2.0, 2.0, 2.0)
x2 = (True, 10.0, 10.0, 2.0, 2.0, 2.0)
xs = [x1, x2]
for x in xs:
par_rslt = black_scholes_parakeet(*x)
py_rslt = black_scholes(*x)
assert eq(par_rslt, py_rslt)
def test_set_1d_simple_slice():
x = np.array([1,2,3,4,5,6])
x2 = x.copy()
idx = slice(2,4)
y = [10, 20]
print x[idx]
x[idx] = y
setidx(x2, idx, y)
assert testing_helpers.eq(x, x2), "Expected %s but got %s" % (x, x2)
def test_rule30():
output = init.copy()
cur = init
zero_array = np.array([0])
indices = np.arange(1,size+1)
for _ in range(size/2):
extended = np.concatenate((zero_array, cur, zero_array))
def run_rule30(i):
return rule30(extended, i)
parakeet_iter = parakeet.map(run_rule30, indices)
cur = np.array(map(run_rule30, indices))
assert eq(parakeet_iter, cur), \
"Parakeet result (%s) didn't match Python result(%s)" % \
(parakeet_iter, cur)
output = np.vstack((output,cur))
def test_rule30():
output = init.copy()
cur = init
zero_array = np.array([0])
indices = np.arange(1, size + 1)
for _ in range(size / 2):
extended = np.concatenate((zero_array, cur, zero_array))
def run_rule30(i):
return rule30(extended, i)
parakeet_iter = parakeet.map(run_rule30, indices)
cur = np.array(map(run_rule30, indices))
assert eq(parakeet_iter, cur), \
"Parakeet result (%s) didn't match Python result(%s)" % \
(parakeet_iter, cur)
output = np.vstack((output, cur))
def test_add1():
result = each(add11d, int_vec)
expected = int_vec + 1
assert testing_helpers.eq(result, expected), \
"Expected %s, got %s" % (expected, result)
def test_2d_reduce():
par_rslt = reduce_2d(a)
np_rslt = np.sum(a, 0)
assert testing_helpers.eq(par_rslt, np_rslt), \
"Expected %s but got %s" % (np_rslt, par_rslt)
def test_avg2d():
x = np.random.randn(20, 30)
testing_helpers.eq(x.mean(), avg2d(x))
def test_add1_external_map():
parakeet_result = each(add1_scalar, ints_1d)
python_result = ints_1d + 1
assert eq(parakeet_result, python_result), \
"Python %s != Parakeet %s" % (python_result, parakeet_result)
def test_avg2d():
x = np.random.randn(20, 30)
testing_helpers.eq(x.mean(), avg2d(x))
def test_add1():
result = each(add11d, int_vec)
expected = int_vec + 1
assert testing_helpers.eq(result, expected), \
"Expected %s, got %s" % (expected, result)
def test_2d_reduce():
par_rslt = reduce_2d(a)
np_rslt = np.sum(a, 0)
assert testing_helpers.eq(par_rslt, np_rslt), \
"Expected %s but got %s" % (np_rslt, par_rslt)
def test_identity():
x = np.ones((1,1,2,1))
def get_idx(idx):
return x[idx]
y = parakeet.imap(get_idx, x.shape)
assert eq(x,y)
def test_fprop(): parakeet_result = fprop(network, X[0]) print "Parakeet result", parakeet_result python_result = fprop_python(network, X[0]) print "Python result", python_result assert testing_helpers.eq(parakeet_result, python_result)
def test_fprop():
parakeet_result = fprop(network, X[0])
print "Parakeet result", parakeet_result
python_result = fprop_python(network, X[0])
print "Python result", python_result
assert testing_helpers.eq(parakeet_result, python_result)
