def test_ufunc_out(self):
from numpypy import array, negative, zeros, sin
from math import sin as msin
a = array([[1, 2], [3, 4]])
c = zeros((2,2,2))
b = negative(a + a, out=c[1])
#test for view, and also test that forcing out also forces b
assert (c[:, :, 1] == [[0, 0], [-4, -8]]).all()
assert (b == [[-2, -4], [-6, -8]]).all()
#Test broadcast, type promotion
b = negative(3, out=a)
assert (a == -3).all()
c = zeros((2, 2), dtype=float)
b = negative(3, out=c)
assert b.dtype.kind == c.dtype.kind
assert b.shape == c.shape
a = array([1, 2])
b = sin(a, out=c)
assert(c == [[msin(1), msin(2)]] * 2).all()
b = sin(a, out=c+c)
assert (c == b).all()
#Test shape agreement
a = zeros((3,4))
b = zeros((3,5))
raises(ValueError, 'negative(a, out=b)')
b = zeros((1,4))
raises(ValueError, 'negative(a, out=b)')
def test_dstack(self):
import numpypy as np
a = np.array((1, 2, 3))
b = np.array((2, 3, 4))
c = np.dstack((a, b))
assert np.array_equal(c, [[[1, 2], [2, 3], [3, 4]]])
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
c = np.dstack((a, b))
assert np.array_equal(c, [[[1, 2]], [[2, 3]], [[3, 4]]])
#skip("https://bugs.pypy.org/issue1394")
for shape1, shape2 in [[(4, 2, 3), (4, 2, 7)], [(7, 2, 0), (7, 2, 10)],
[(7, 2, 0), (7, 2, 0)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(
np.dstack((a, b)) == np.ones((a.shape[0], a.shape[1],
a.shape[2] + b.shape[2])))
for shape1, shape2 in [[(4, 2, 3, 5), (4, 2, 7, 5)],
[(7, 2, 0, 5), (7, 2, 10, 5)],
[(7, 2, 0, 5), (7, 2, 0, 5)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(
np.dstack((a, b)) == np.ones((a.shape[0], a.shape[1],
a.shape[2] + b.shape[2],
a.shape[3])))
def test_sort_corner_cases_string_records(self):
skip('not implemented yet')
from numpypy import array, dtype
# test string sorts.
s = 'aaaaaaaa'
a = array([s + chr(i) for i in range(101)])
b = a[::-1].copy()
for kind in ['q', 'm', 'h'] :
msg = "string sort, kind=%s" % kind
c = a.copy();
c.sort(kind=kind)
assert (c == a).all(), msg
c = b.copy();
c.sort(kind=kind)
assert (c == a).all(), msg
# test record array sorts.
dt =dtype([('f', float), ('i', int)])
a = array([(i, i) for i in range(101)], dtype = dt)
b = a[::-1]
for kind in ['q', 'h', 'm'] :
msg = "object sort, kind=%s" % kind
c = a.copy();
c.sort(kind=kind)
assert (c == a).all(), msg
c = b.copy();
c.sort(kind=kind)
assert (c == a).all(), msg
def reset_node_output_values(self, outputValues):
if self.isOutputLayer:
self.nodesOutput = np.array(outputValues)
else:
#Dont change the bias node value ever
self.nodesOutput[:-1] = np.array(outputValues[:-1])
def test_choose_basic(self):
from numpypy import array, choose
a, b, c = array([1, 2, 3]), array([4, 5, 6]), array([7, 8, 9])
r = array([2, 1, 0]).choose([a, b, c])
assert (r == [7, 5, 3]).all()
r = choose(array([2, 1, 0]), [a, b, c])
assert (r == [7, 5, 3]).all()
def test_index_int64(self):
from numpypy import array, int64
a = array(range(10), dtype=int64)
b = array([0] * 10, dtype=int64)
for idx in b:
a[idx] += 1
def test_ufunc_cast(self):
from _numpypy import array, negative, add, sum
a = array(16, dtype = int)
c = array(0, dtype = float)
b = negative(a, out=c)
assert b == c
b = add(a, a, out=c)
assert b == c
d = array([16, 16], dtype=int)
b = sum(d, out=c)
assert b == c
try:
from _numpypy import version
v = version.version.split('.')
except:
v = ['1', '6', '0'] # numpypy is api compatable to what version?
if v[0]<'2':
b = negative(c, out=a)
assert b == a
b = add(c, c, out=a)
assert b == a
b = sum(array([16, 16], dtype=float), out=a)
assert b == a
else:
cast_error = raises(TypeError, negative, c, a)
assert str(cast_error.value) == \
"Cannot cast ufunc negative output from dtype('float64') to dtype('int64') with casting rule 'same_kind'"
def test_dstack(self):
import numpypy as np
a = np.array((1, 2, 3))
b = np.array((2, 3, 4))
c = np.dstack((a, b))
assert np.array_equal(c, [[[1, 2], [2, 3], [3, 4]]])
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
c = np.dstack((a, b))
assert np.array_equal(c, [[[1, 2]], [[2, 3]], [[3, 4]]])
#skip("https://bugs.pypy.org/issue1394")
for shape1, shape2 in [[(4, 2, 3), (4, 2, 7)],
[(7, 2, 0), (7, 2, 10)],
[(7, 2, 0), (7, 2, 0)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.dstack((a, b)) ==
np.ones((a.shape[0],
a.shape[1],
a.shape[2] + b.shape[2])))
for shape1, shape2 in [[(4, 2, 3, 5), (4, 2, 7, 5)],
[(7, 2, 0, 5), (7, 2, 10, 5)],
[(7, 2, 0, 5), (7, 2, 0, 5)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.dstack((a, b)) ==
np.ones((a.shape[0],
a.shape[1],
a.shape[2] + b.shape[2],
a.shape[3])))
def test_conj(self):
from numpypy import array
a = array([1 + 2j, 1 - 2j])
assert (a.conj() == [1 - 2j, 1 + 2j]).all()
a = array([1,2,3.4J],dtype=complex)
assert a[2].conjugate() == 0-3.4j
def unpack(self, weight_list):
# This method will create a list of weight matrices. Each list element
# corresponds to the connection between two layers.
if self.n_hidden_layers == 0:
return [
np.array(weight_list).reshape(self.n_inputs + 1,
self.n_outputs)
]
else:
weight_layers = [
np.array(weight_list[:(self.n_inputs + 1) *
self.n_hiddens]).reshape(
self.n_inputs + 1, self.n_hiddens)
]
weight_layers += [
np.array(weight_list[
(self.n_inputs + 1) * self.n_hiddens +
(i * (self.n_hiddens**2 + self.n_hiddens)):
(self.n_inputs + 1) * self.n_hiddens +
((i + 1) * (self.n_hiddens**2 + self.n_hiddens))]).reshape(
self.n_hiddens + 1, self.n_hiddens)
for i in xrange(self.n_hidden_layers - 1)
]
weight_layers += [
np.array(weight_list[(self.n_inputs + 1) * self.n_hiddens + (
(self.n_hidden_layers - 1) *
(self.n_hiddens**2 + self.n_hiddens)):]).reshape(
self.n_hiddens + 1, self.n_outputs)
]
return weight_layers
def test_ufunc_cast(self):
from _numpypy import array, negative, add, sum
a = array(16, dtype=int)
c = array(0, dtype=float)
b = negative(a, out=c)
assert b == c
b = add(a, a, out=c)
assert b == c
d = array([16, 16], dtype=int)
b = sum(d, out=c)
assert b == c
try:
from _numpypy import version
v = version.version.split('.')
except:
v = ['1', '6', '0'] # numpypy is api compatable to what version?
if v[0] < '2':
b = negative(c, out=a)
assert b == a
b = add(c, c, out=a)
assert b == a
b = sum(array([16, 16], dtype=float), out=a)
assert b == a
else:
cast_error = raises(TypeError, negative, c, a)
assert str(cast_error.value) == \
"Cannot cast ufunc negative output from dtype('float64') to dtype('int64') with casting rule 'same_kind'"
def test_div_other(self):
from numpypy import array
a = array(range(5))
b = array([2, 2, 2, 2, 2], float)
c = a / b
for i in range(5):
assert c[i] == i / 2.0
def test_mod_other(self):
from numpypy import array
a = array(range(5))
b = array([2, 2, 2, 2, 2])
c = a % b
for i in range(5):
assert c[i] == i % 2
def test_pow_other(self):
from numpypy import array
a = array(range(5), float)
b = array([2, 2, 2, 2, 2])
c = a ** b
for i in range(5):
assert c[i] == i ** 2
def test_vstack(self):
import numpypy as np
a = np.array([1, 2, 3])
b = np.array([2, 3, 4])
c = np.vstack((a, b))
assert np.array_equal(c, [[1, 2, 3],
[2, 3, 4]])
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
c = np.vstack((a, b))
assert np.array_equal(c, [[1],
[2],
[3],
[2],
[3],
[4]])
for shape1, shape2 in [[(2, 1), (3, 1)],
[(2, 4), [3, 4]]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.vstack((a, b)) ==
np.ones((a.shape[0] + b.shape[0],
a.shape[1])))
#skip("https://bugs.pypy.org/issue1394")
for shape1, shape2 in [[(3, 2, 4), (7, 2, 4)],
[(0, 2, 7), (10, 2, 7)],
[(0, 2, 7), (0, 2, 7)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.vstack((a, b)) ==
np.ones((a.shape[0] + b.shape[0],
a.shape[1],
a.shape[2])))
def test_hstack(self):
import numpypy as np
a = np.array((1, 2, 3))
b = np.array((2, 3, 4))
c = np.hstack((a, b))
assert np.array_equal(c, [1, 2, 3, 2, 3, 4])
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
c = np.hstack((a, b))
assert np.array_equal(c, [[1, 2],
[2, 3],
[3, 4]])
for shape1, shape2 in [[(1, 2), (1, 3)],
[(4, 2), (4, 3)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.hstack((a, b)) ==
np.ones((a.shape[0],
a.shape[1] + b.shape[1])))
#skip("https://bugs.pypy.org/issue1394")
for shape1, shape2 in [[(2, 3, 4), (2, 7, 4)],
[(1, 4, 7), (1, 10, 7)],
[(1, 4, 7), (1, 0, 7)],
[(1, 0, 7), (1, 0, 7)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.hstack((a, b)) ==
np.ones((a.shape[0],
a.shape[1] + b.shape[1],
a.shape[2])))
def test_vstack(self):
import numpypy as np
a = np.array([1, 2, 3])
b = np.array([2, 3, 4])
c = np.vstack((a, b))
assert np.array_equal(c, [[1, 2, 3],
[2, 3, 4]])
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
c = np.vstack((a, b))
assert np.array_equal(c, [[1],
[2],
[3],
[2],
[3],
[4]])
for shape1, shape2 in [[(2, 1), (3, 1)],
[(2, 4), [3, 4]]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.vstack((a, b)) ==
np.ones((a.shape[0] + b.shape[0],
a.shape[1])))
#skip("https://bugs.pypy.org/issue1394")
for shape1, shape2 in [[(3, 2, 4), (7, 2, 4)],
[(0, 2, 7), (10, 2, 7)],
[(0, 2, 7), (0, 2, 7)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.vstack((a, b)) ==
np.ones((a.shape[0] + b.shape[0],
a.shape[1],
a.shape[2])))
def test_subtract_other(self):
from numpypy import array
a = array(range(5))
b = array([1, 1, 1, 1, 1])
c = a - b
for i in range(5):
assert c[i] == i - 1
def test_power_float(self):
import math
from numpypy import power, array
a = array([1., 2., 3.])
b = power(a, 3)
for i in range(len(a)):
assert b[i] == a[i] ** 3
a = array([1., 2., 3.])
b = array([1., 2., 3.])
c = power(a, b)
for i in range(len(a)):
assert c[i] == a[i] ** b[i]
assert power(2, float('inf')) == float('inf')
assert power(float('inf'), float('inf')) == float('inf')
assert power(12345.0, 12345.0) == float('inf')
assert power(-12345.0, 12345.0) == float('-inf')
assert power(-12345.0, 12346.0) == float('inf')
assert math.isnan(power(-1, 1.1))
assert math.isnan(power(-1, -1.1))
assert power(-2.0, -1) == -0.5
assert power(-2.0, -2) == 0.25
assert power(12345.0, -12345.0) == 0
assert power(float('-inf'), 2) == float('inf')
assert power(float('-inf'), 2.5) == float('inf')
assert power(float('-inf'), 3) == float('-inf')
def test_abstract_types(self):
import numpypy as numpy
raises(TypeError, numpy.generic, 0)
raises(TypeError, numpy.number, 0)
raises(TypeError, numpy.integer, 0)
exc = raises(TypeError, numpy.signedinteger, 0)
assert 'cannot create' in str(exc.value)
assert 'signedinteger' in str(exc.value)
exc = raises(TypeError, numpy.unsignedinteger, 0)
assert 'cannot create' in str(exc.value)
assert 'unsignedinteger' in str(exc.value)
raises(TypeError, numpy.floating, 0)
raises(TypeError, numpy.inexact, 0)
# numpy allows abstract types in array creation
a_n = numpy.array([4,4], numpy.number)
a_i = numpy.array([4,4], numpy.integer)
a_s = numpy.array([4,4], numpy.signedinteger)
a_u = numpy.array([4,4], numpy.unsignedinteger)
assert a_n.dtype.num == 12
assert a_i.dtype.num == 7
assert a_s.dtype.num == 7
assert a_u.dtype.num == 8
assert a_n.dtype is numpy.dtype('float64')
if self.ptr_size == 4:
assert a_i.dtype is numpy.dtype('int32')
assert a_s.dtype is numpy.dtype('int32')
assert a_u.dtype is numpy.dtype('uint32')
else:
assert a_i.dtype is numpy.dtype('int64')
assert a_s.dtype is numpy.dtype('int64')
assert a_u.dtype is numpy.dtype('uint64')
def test_dot(self):
from numpypy import array
a = array(range(5))
assert a.dot(a) == 30.0
a = array(range(5))
assert a.dot(range(5)) == 30
def test_amin(self):
# tests taken from numpy/core/fromnumeric.py docstring
from numpypy import array, arange, amin, zeros
a = arange(4).reshape((2,2))
assert amin(a) == 0
# # Minima along the first axis
# assert (amin(a, axis=0) == array([0, 1])).all()
# # Minima along the second axis
# assert (amin(a, axis=1) == array([0, 2])).all()
# # NaN behaviour
# b = arange(5, dtype=float)
# b[2] = NaN
# assert amin(b) == nan
# assert nanmin(b) == 0.0
assert amin(range(10)) == 0
assert amin(array(range(10))) == 0
assert list(amin(zeros((0, 2)), axis=1)) == []
a = array([[1, 2], [3, 4]])
out = array([[0, 0], [0, 0]])
c = amin(a, axis=1, out=out[0])
assert (c == [1, 3]).all()
assert (c == out[0]).all()
assert (c != out[1]).all()
def test_add_other(self):
from numpypy import array
a = array(range(5))
b = array(range(4, -1, -1))
c = a + b
for i in range(5):
assert c[i] == 4
def test_dot(self):
from numpypy import array, dot, arange
a = array(range(5))
assert dot(a, a) == 30.0
a = array(range(5))
assert a.dot(range(5)) == 30
assert dot(range(5), range(5)) == 30
assert (dot(5, [1, 2, 3]) == [5, 10, 15]).all()
a = arange(12).reshape(3, 4)
b = arange(12).reshape(4, 3)
c = a.dot(b)
assert (c == [[ 42, 48, 54], [114, 136, 158], [186, 224, 262]]).all()
a = arange(24).reshape(2, 3, 4)
raises(ValueError, "a.dot(a)")
b = a[0, :, :].T
#Superfluous shape test makes the intention of the test clearer
assert a.shape == (2, 3, 4)
assert b.shape == (4, 3)
c = dot(a, b)
assert (c == [[[14, 38, 62], [38, 126, 214], [62, 214, 366]],
[[86, 302, 518], [110, 390, 670], [134, 478, 822]]]).all()
c = dot(a, b[:, 2])
assert (c == [[62, 214, 366], [518, 670, 822]]).all()
a = arange(3*2*6).reshape((3,2,6))
b = arange(3*2*6)[::-1].reshape((2,6,3))
assert dot(a, b)[2,0,1,2] == 1140
assert (dot([[1,2],[3,4]],[5,6]) == [17, 39]).all()
def test_fromnumeric(self):
from numpypy import array, swapaxes
x = array([[1,2,3]])
assert (swapaxes(x,0,1) == array([[1], [2], [3]])).all()
x = array([[[0,1],[2,3]],[[4,5],[6,7]]])
assert (swapaxes(x,0,2) == array([[[0, 4], [2, 6]],
[[1, 5], [3, 7]]])).all()
def test_amin(self):
# tests taken from numpy/core/fromnumeric.py docstring
from numpypy import array, arange, amin, zeros
a = arange(4).reshape((2,2))
assert amin(a) == 0
# # Minima along the first axis
# assert (amin(a, axis=0) == array([0, 1])).all()
# # Minima along the second axis
# assert (amin(a, axis=1) == array([0, 2])).all()
# # NaN behaviour
# b = arange(5, dtype=float)
# b[2] = NaN
# assert amin(b) == nan
# assert nanmin(b) == 0.0
assert amin(range(10)) == 0
assert amin(array(range(10))) == 0
assert list(amin(zeros((0, 2)), axis=1)) == []
a = array([[1, 2], [3, 4]])
out = array([[0, 0], [0, 0]])
c = amin(a, axis=1, out=out[0])
assert (c == [1, 3]).all()
assert (c == out[0]).all()
assert (c != out[1]).all()
def test_add_other(self):
from numpypy import array
a = array(range(5))
b = array([i for i in reversed(range(5))])
c = a + b
for i in range(5):
assert c[i] == 4
def test_fromnumeric(self):
from numpypy import array, swapaxes
x = array([[1,2,3]])
assert (swapaxes(x,0,1) == array([[1], [2], [3]])).all()
x = array([[[0,1],[2,3]],[[4,5],[6,7]]])
assert (swapaxes(x,0,2) == array([[[0, 4], [2, 6]],
[[1, 5], [3, 7]]])).all()
def test_hstack(self):
import numpypy as np
a = np.array((1, 2, 3))
b = np.array((2, 3, 4))
c = np.hstack((a, b))
assert np.array_equal(c, [1, 2, 3, 2, 3, 4])
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
c = np.hstack((a, b))
assert np.array_equal(c, [[1, 2],
[2, 3],
[3, 4]])
for shape1, shape2 in [[(1, 2), (1, 3)],
[(4, 2), (4, 3)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.hstack((a, b)) ==
np.ones((a.shape[0],
a.shape[1] + b.shape[1])))
#skip("https://bugs.pypy.org/issue1394")
for shape1, shape2 in [[(2, 3, 4), (2, 7, 4)],
[(1, 4, 7), (1, 10, 7)],
[(1, 4, 7), (1, 0, 7)],
[(1, 0, 7), (1, 0, 7)]]:
a, b = np.ones(shape1), np.ones(shape2)
assert np.all(np.hstack((a, b)) ==
np.ones((a.shape[0],
a.shape[1] + b.shape[1],
a.shape[2])))
def test_ufunc_out(self):
from _numpypy import array, negative, zeros, sin
from math import sin as msin
a = array([[1, 2], [3, 4]])
c = zeros((2, 2, 2))
b = negative(a + a, out=c[1])
#test for view, and also test that forcing out also forces b
assert (c[:, :, 1] == [[0, 0], [-4, -8]]).all()
assert (b == [[-2, -4], [-6, -8]]).all()
#Test broadcast, type promotion
b = negative(3, out=a)
assert (a == -3).all()
c = zeros((2, 2), dtype=float)
b = negative(3, out=c)
assert b.dtype.kind == c.dtype.kind
assert b.shape == c.shape
a = array([1, 2])
b = sin(a, out=c)
assert (c == [[msin(1), msin(2)]] * 2).all()
b = sin(a, out=c + c)
assert (c == b).all()
#Test shape agreement
a = zeros((3, 4))
b = zeros((3, 5))
raises(ValueError, 'negative(a, out=b)')
b = zeros((1, 4))
raises(ValueError, 'negative(a, out=b)')
def test_sum(self):
# tests taken from numpy/core/fromnumeric.py docstring
from numpypy import array, sum, ones
assert sum([0.5, 1.5])== 2.0
assert sum([[0, 1], [0, 5]]) == 6
# assert sum([0.5, 0.7, 0.2, 1.5], dtype=int32) == 1
assert (sum([[0, 1], [0, 5]], axis=0) == array([0, 6])).all()
assert (sum([[0, 1], [0, 5]], axis=1) == array([1, 5])).all()
def test_add(self):
from numpypy import array, add
a = array([-5.0, -0.0, 1.0])
b = array([ 3.0, -2.0,-3.0])
c = add(a, b)
for i in range(3):
assert c[i] == a[i] + b[i]
def test_non_native(self):
from numpypy import array
a = array([1, 2, 3], dtype=self.non_native_prefix + 'g') # longdouble
assert a[0] == 1
assert (a + a)[1] == 4
a = array([1, 2, 3], dtype=self.non_native_prefix + 'G') # clongdouble
assert a[0] == 1
assert (a + a)[1] == 4
def test_multiply(self):
from numpypy import array, multiply
a = array([-5.0, -0.0, 1.0])
b = array([ 3.0, -2.0,-3.0])
c = multiply(a, b)
for i in range(3):
assert c[i] == a[i] * b[i]
def test_minimum(self):
from numpypy import array, minimum
a = array([-5.0, -0.0, 1.0])
b = array([ 3.0, -2.0,-3.0])
c = minimum(a, b)
for i in range(3):
assert c[i] == min(a[i], b[i])
def test_subtract(self):
from numpypy import array, subtract
a = array([-5.0, -0.0, 1.0])
b = array([ 3.0, -2.0,-3.0])
c = subtract(a, b)
for i in range(3):
assert c[i] == a[i] - b[i]
def test_all(self):
from numpypy import array
a = array(range(5))
assert a.all() == False
a[0] = 3.0
assert a.all() == True
b = array([])
assert b.all() == True
def test_any(self):
from numpypy import array, zeros
a = array(range(5))
assert a.any() == True
b = zeros(5)
assert b.any() == False
c = array([])
assert c.any() == False
def test_sum(self):
from numpypy import array
a = array(range(5))
assert a.sum() == 10.0
assert a[:4].sum() == 6.0
a = array([True] * 5, bool)
assert a.sum() == 5
def test_mismatched_shape(self):
from numpypy import array, array_equal
a = [1, 2, 3]
b = [[1, 2, 3], [1, 2, 3]]
assert not array_equal(a, b)
assert not array_equal(a, array(b))
assert not array_equal(array(a), b)
assert not array_equal(array(a), array(b))
def test_not_equal(self):
from numpypy import array, array_equal
a = [1, 2, 3]
b = [1, 2, 4]
assert not array_equal(a, b)
assert not array_equal(a, array(b))
assert not array_equal(array(a), b)
assert not array_equal(array(a), array(b))
def test_repr_2(self):
from numpypy import array, zeros
int_size = array(5).dtype.itemsize
a = array(range(5), float)
assert repr(a) == "array([ 0., 1., 2., 3., 4.])"
a = array([], float)
assert repr(a) == "array([], dtype=float64)"
a = zeros(1001)
assert repr(a) == "array([ 0., 0., 0., ..., 0., 0., 0.])"
a = array(range(5), int)
if a.dtype.itemsize == int_size:
assert repr(a) == "array([0, 1, 2, 3, 4])"
else:
assert repr(a) == "array([0, 1, 2, 3, 4], dtype=int64)"
a = array(range(5), 'int32')
if a.dtype.itemsize == int_size:
assert repr(a) == "array([0, 1, 2, 3, 4])"
else:
assert repr(a) == "array([0, 1, 2, 3, 4], dtype=int32)"
a = array([], long)
assert repr(a) == "array([], dtype=int64)"
a = array([True, False, True, False], "?")
assert repr(a) == "array([ True, False, True, False], dtype=bool)"
a = zeros([])
assert repr(a) == "array(0.0)"
a = array(0.2)
assert repr(a) == "array(0.2)"
a = array([2])
assert repr(a) == "array([2])"
def test_sort_nonnative(self):
from numpypy import array
nnp = self.non_native_prefix
for dtype in [ nnp + 'i2']:
a = array([6, 4, -1, 3, 8, 3, 256+20, 100, 101], dtype=dtype)
b = array([-1, 3, 3, 4, 6, 8, 100, 101, 256+20], dtype=dtype)
c = a.copy()
import sys
if '__pypy__' in sys.builtin_module_names:
exc = raises(NotImplementedError, a.sort)
assert exc.value[0].find('supported') >= 0
def test_applevel(self):
from _numpypy import array, sum, max, min
a = array([[1, 2], [3, 4]])
out = array([[0, 0], [0, 0]])
c = sum(a, axis=0, out=out[0])
assert (c == [4, 6]).all()
assert (c == out[0]).all()
assert (c != out[1]).all()
c = max(a, axis=1, out=out[0])
assert (c == [2, 4]).all()
assert (c == out[0]).all()
assert (c != out[1]).all()
def test_argsort_axis(self):
from numpypy import array
a = array([])
for axis in [None, -1, 0]:
assert a.argsort(axis=axis).shape == (0,)
a = array([[4, 2], [1, 3]])
assert (a.argsort(axis=None) == [2, 1, 3, 0]).all()
assert (a.argsort(axis=-1) == [[1, 0], [0, 1]]).all()
assert (a.argsort(axis=0) == [[1, 0], [0, 1]]).all()
assert (a.argsort(axis=1) == [[1, 0], [0, 1]]).all()
a = array([[3, 2, 1], [1, 2, 3]])
assert (a.argsort(axis=0) == [[1, 0, 0], [0, 1, 1]]).all()
assert (a.argsort(axis=1) == [[2, 1, 0], [0, 1, 2]]).all()
def test_str_slice(self):
from numpypy import array, zeros
a = array(range(5), float)
b = a[1::2]
assert str(b) == "[ 1. 3.]"
a = zeros(2002)
b = a[::2]
assert str(b) == "[ 0. 0. 0. ..., 0. 0. 0.]"
a = array((range(5), range(5, 10)), dtype="int16")
b = a[1, 2:]
assert str(b) == "[7 8 9]"
b = a[2:1, ]
assert str(b) == "[]"
def test_repr_slice(self):
from numpypy import array, zeros
a = array(range(5), float)
b = a[1::2]
assert repr(b) == "array([ 1., 3.])"
a = zeros(2002)
b = a[::2]
assert repr(b) == "array([ 0., 0., 0., ..., 0., 0., 0.])"
a = array((range(5), range(5, 10)), dtype="int16")
b = a[1, 2:]
assert repr(b) == "array([7, 8, 9], dtype=int16)"
# an empty slice prints its shape
b = a[2:1, ]
assert repr(b) == "array([], shape=(0, 5), dtype=int16)"
def __init__(self, argument, id=None):
if isinstance(argument, int):
super(EncodedSequence, self).__init__(
np.zeros(argument, int), id)
self.position = 0
else:
if isinstance(argument, np.ndarray) \
and argument.dtype.name.startswith('int'):
super(EncodedSequence, self).__init__(
np.array(argument), id)
else:
super(EncodedSequence, self).__init__(
np.array(list(argument), int), id)
self.position = len(self.elements)
def test_argsort_ndim(self):
from numpypy import array
a = array([[4, 2], [1, 3]])
assert (a.argsort() == [[1, 0], [0, 1]]).all()
a = array(range(10) + range(10) + range(10))
b = a.argsort()
assert (b[:3] == [0, 10, 20]).all()
#trigger timsort 'run' mode which calls arg_getitem_slice
a = array(range(100) + range(100) + range(100))
b = a.argsort()
assert (b[:3] == [0, 100, 200]).all()
a = array([[[]]]).reshape(3,4,0)
b = a.argsort()
assert b.size == 0
def test_vdot(self):
import numpypy as np
a = np.array([1 + 2j, 3 + 4j])
b = np.array([5 + 6j, 7 + 8j])
c = np.vdot(a, b)
assert c == (70 - 8j)
c = np.vdot(b, a)
assert c == (70 + 8j)
a = np.array([[1, 4], [5, 6]])
b = np.array([[4, 1], [2, 2]])
c = np.vdot(a, b)
assert c == 30
c = np.vdot(b, a)
assert c == 30
def solver(infile,
testcase,
N=None,
P=None,
I=None,
T=None,
S=None,
C=None,
**ignore):
#import collections as co
#import functools as ft
#import itertools as it
#import operator as op
#import math as ma
#import re
import numpypy as np
#import scipy as sp
#import networkx as nx
S = np.array(S)
done = np.zeros(P, dtype=int)
for row in range(P[0]):
m = S[row].max()
done[row][S[row] == m] = 1
for col in range(P[1]):
m = S[:, col].max()
done[:, col][S[:, col] == m] = 1
res = 'YES' if done.sum() == P[0] * P[1] else 'NO'
return 'Case #%s: %s\n' % (testcase, res)
def test_outer(self):
from numpypy import array, outer
a = [1, 2, 3]
b = [4, 5, 6]
res = outer(a, b)
expected = array([[4, 5, 6], [8, 10, 12], [12, 15, 18]])
assert (res == expected).all()
def test():
def check(x,y):
a = pearsonr(x,y)
b = np.cor(x,y)
print "pearsonr:", a
print "corcoef:", b
assert t_round_equal(a,b)
x = np.array([1,2,3,5,9,12])
y = x*2
y[3] *= 1.5
y[0] *= 0.6
check(x,y)
import random
x = np.array([random.random() for _ in range(10)])
y = np.array([random.random() for _ in range(10)])
check(x,y)
def test_transpose(self):
from numpypy import arange, array, transpose, ones
x = arange(4).reshape((2,2))
assert (transpose(x) == array([[0, 2],[1, 3]])).all()
# Once axes argument is implemented, add more tests
import sys
if '__pypy__' in sys.builtin_module_names:
raises(NotImplementedError, "transpose(x, axes=(1, 0, 2))")
def test_str(self):
from numpypy import array, zeros
a = array(range(5), float)
assert str(a) == "[ 0. 1. 2. 3. 4.]"
assert str((2 * a)[:]) == "[ 0. 2. 4. 6. 8.]"
a = zeros(1001)
assert str(a) == "[ 0. 0. 0. ..., 0. 0. 0.]"
a = array(range(5), dtype=long)
assert str(a) == "[0 1 2 3 4]"
a = array([True, False, True, False], dtype="?")
assert str(a) == "[ True False True False]"
a = array(range(5), dtype="int8")
assert str(a) == "[0 1 2 3 4]"
a = array(range(5), dtype="int16")
assert str(a) == "[0 1 2 3 4]"
a = array((range(5), range(5, 10)), dtype="int16")
assert str(a) == "[[0 1 2 3 4]\n [5 6 7 8 9]]"
a = array(3, dtype=int)
assert str(a) == "3"
a = zeros((400, 400), dtype=int)
assert str(
a
) == '[[0 0 0 ..., 0 0 0]\n [0 0 0 ..., 0 0 0]\n [0 0 0 ..., 0 0 0]\n ..., \n [0 0 0 ..., 0 0 0]\n [0 0 0 ..., 0 0 0]\n [0 0 0 ..., 0 0 0]]'
a = zeros((2, 2, 2))
r = str(a)
assert r == '[[[ 0. 0.]\n [ 0. 0.]]\n\n [[ 0. 0.]\n [ 0. 0.]]]'
def test_argsort_dtypes(self):
from numpypy import array, arange
assert array(2.0).argsort() == 0
nnp = self.non_native_prefix
for dtype in ['int', 'float', 'int16', 'float32', 'uint64',
nnp + 'i2', complex]:
a = array([6, 4, -1, 3, 8, 3, 256+20, 100, 101], dtype=dtype)
exp = list(a)
exp = sorted(range(len(exp)), key=exp.__getitem__)
c = a.copy()
res = a.argsort()
assert (res == exp).all(), '%r\n%r\n%r' % (a,res,exp)
assert (a == c).all() # not modified
a = arange(100, dtype=dtype)
assert (a.argsort() == a).all()
import sys
if '__pypy__' in sys.builtin_module_names:
raises(NotImplementedError, 'arange(10,dtype="float16").argsort()')
def main(zods):
all_zods = [aqu,pis,ari,tau,gem,can,leo,vir,lib,sco,sag,cap]
scoring_matrix = numpy.array(((0,5,10,15,10,5,0,-5,-10,-15,-10,-5),
(5,0,-5,-10,-15,-10,-5,0,5,10,15,10),
(10,-5,0,5,-10,15,-10,5,0,-5,10,-15),
(15,-10,5,0,-5,10,-15,10,-5,0,5,-10),
(10,-15,-10,-5,0,5,-10,15,10,5,0,-5),
(5,-10,15,10,5,0,-5,-10,-15,10,-5,0),
(0,-5,-10,-15,-10,-5,0,5,10,15,10,5),
(-5,0,5,10,15,-10,5,0,-5,-10,-15,10),
(-10,5,0,-5,10,-15,10,-5,0,5,-10,15),
(-15,10,-5,0,5,10,15,-10,5,0,-5,-10),
(-10,15,10,5,0,-5,10,-15,-10,-5,0,5),
(-5,10,-15,-10,-5,0,5,10,15,-10,5,0)))
def score(combo,combo_proc_seq,combo_len):
running_score = [0]*combo_len
for i in combo_proc_seq:
running_score[i[0]] = scoring_matrix[combo[i[0]],combo[i[1]]] + scoring_matrix[combo[i[0]],combo[i[2]]]
return running_score
def combo_to_name(combo):
naming = ['Aquarius','Pisces','Aries','Taurus','Gemini','Cancer','Leo','Virgo','Libra','Scorpio','Sagittarius','Capricorn']
combo_name = []
for i in combo:
combo_name.append(naming[i])
return combo_name
running_best_combo = []
running_best_combo_scores = []
running_best_score_sum = -1000
combo_proc_seq = []
combo_len = len(zods)
number_of_missing_zods = 10-combo_len
for i in range(0,10):
combo_proc_seq.append([i,int((i+1)%10),i-1])
filler_combos = itertools.combinations_with_replacement(all_zods,number_of_missing_zods)
for filler in filler_combos:
combos = itertools.permutations(list(filler)+zods)
for combo in combos:
combo_score = score(combo,combo_proc_seq,10)
sum_combo_score = sum(combo_score)
if sum_combo_score >= running_best_score_sum:
running_best_score_sum = sum_combo_score
running_best_combo = combo
running_best_combo_scores = combo_score
## if sum(combo_score) == running_best_score_sum:
## running_best_combo.append(combo_to_name(combo))
## running_best_combo_scores.append(combo_score)
print_out = []
names = combo_to_name(running_best_combo)
for i in range(0,len(names)):
print_out.append('%s - %s'%(names[i],running_best_combo_scores[i]))
print running_best_score_sum, print_out
def solver(testcase, N=None, P=None, I=None, T=None, S=None, C=None, **ignore):
#import collections as co
#import functools as ft
#import itertools as it
#import operator as op
#import math as ma
#import re
import numpypy as np
#import scipy as sp
#import networkx as nx
e, R, N = P
I = np.array(I)
E = np.array([0] * len(I))
E[0] = e
nexts = reversed(I.argsort())
res = 0
for current in nexts:
e_next = R
for nex in xrange(current + 1, len(E)):
ediff = R * (nex - current - 1)
if ediff >= e:
break
if E[nex] > 0:
e_next = max(R, E[nex] - ediff)
break
if current + 1 < len(E) and E[current + 1] == 0:
E[current + 1] = e_next
if E[current] == 0:
for prev in xrange(current - 1, -1, -1):
ediff = R * (current - prev)
if E[prev] > 0 or ediff >= e:
E[current] = min(e, E[prev] + ediff)
break
res += (E[current] + R - e_next) * I[current]
return 'Case #%s: %s\n' % (testcase, res)
def test_reduce_out(self):
from numpypy import arange, zeros, array
a = arange(15).reshape(5, 3)
b = arange(12).reshape(4, 3)
c = a.sum(0, out=b[1])
assert (c == [30, 35, 40]).all()
assert (c == b[1]).all()
raises(ValueError, 'a.prod(0, out=arange(10))')
a = arange(12).reshape(3, 2, 2)
raises(ValueError, 'a.sum(0, out=arange(12).reshape(3,2,2))')
raises(ValueError, 'a.sum(0, out=arange(3))')
c = array([-1, 0, 1]).sum(out=zeros([], dtype=bool))
#You could argue that this should product False, but
# that would require an itermediate result. Cpython numpy
# gives True.
assert c == True
a = array([[-1, 0, 1], [1, 0, -1]])
c = a.sum(0, out=zeros((3, ), dtype=bool))
assert (c == [True, False, True]).all()
c = a.sum(1, out=zeros((2, ), dtype=bool))
assert (c == [True, True]).all()