def fine_grained_binary_search_targeted(model,
x0,
y0,
t,
theta,
initial_lbd=1.0):
nquery = 0
lbd = initial_lbd
while predict(model, x0 + lbd * theta) != t:
lbd *= 1.05
nquery += 1
if lbd > 100:
return float('inf'), nquery
num_intervals = 100
lambdas = np.linspace(0.0, lbd, num_intervals)[1:]
lbd_hi = lbd
lbd_hi_index = 0
for i, lbd in enumerate(lambdas):
nquery += 1
if predict(model, x0 + lbd * theta) == t:
lbd_hi = lbd
lbd_hi_index = i
break
lbd_lo = lambdas[lbd_hi_index - 1]
while (lbd_hi - lbd_lo) > 1e-7:
lbd_mid = (lbd_lo + lbd_hi) / 2.0
nquery += 1
if predict(model, x0 + lbd_mid * theta) == t:
lbd_hi = lbd_mid
else:
lbd_lo = lbd_mid
return lbd_hi, nquery
def test_ufunc():
x = np.array([-1.2, 1.2])
np.absolute(x)
np.absolute(1.2 + 1j)
x = np.linspace(start=-10, stop=10, num=101)
np.add(1.0, 4.0)
x1 = np.arange(9.0).reshape((3, 3))
x2 = np.arange(3.0)
np.add(x1, x2)
np.arccos([1, -1])
x = np.linspace(-1, 1, num=100)
np.arccosh([np.e, 10.0])
np.arccosh(1)
np.arcsin(0)
np.arcsinh(np.array([np.e, 10.0]))
np.arctan([0, 1])
np.pi / 4
x = np.linspace(-10, 10)
x = np.array([-1, +1, +1, -1])
y = np.array([-1, -1, +1, +1])
np.arctan2(y, x) * 180 / np.pi
np.arctan2([1., -1.], [0., 0.])
np.arctan2([0., 0., np.inf], [+0., -0., np.inf])
np.arctanh([0, -0.5])
np.bitwise_and(13, 17)
np.bitwise_and(14, 13)
# np.binary_repr(12) return str
np.bitwise_and([14, 3], 13)
np.bitwise_and([11, 7], [4, 25])
np.bitwise_and(np.array([2, 5, 255]), np.array([3, 14, 16]))
np.bitwise_and([True, True], [False, True])
np.bitwise_or(13, 16)
# np.binary_repr(29)
np.bitwise_or(32, 2)
np.bitwise_or([33, 4], 1)
np.bitwise_or([33, 4], [1, 2])
np.bitwise_or(np.array([2, 5, 255]), np.array([4, 4, 4]))
# np.array([2, 5, 255]) | np.array([4, 4, 4])
np.bitwise_or(np.array([2, 5, 255, 2147483647], dtype=np.int32),
np.array([4, 4, 4, 2147483647], dtype=np.int32))
np.bitwise_or([True, True], [False, True])
np.bitwise_xor(13, 17)
# np.binary_repr(28)
np.bitwise_xor(31, 5)
np.bitwise_xor([31, 3], 5)
np.bitwise_xor([31, 3], [5, 6])
np.bitwise_xor([True, True], [False, True])
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.ceil(a)
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.trunc(a)
np.cos(np.array([0, np.pi / 2, np.pi]))
np.cosh(0)
x = np.linspace(-4, 4, 1000)
rad = np.arange(12.) * np.pi / 6
np.degrees(rad)
out = np.zeros((rad.shape))
r = np.degrees(rad, out)
# np.all(r == out) return bool
np.rad2deg(np.pi / 2)
np.divide(2.0, 4.0)
x1 = np.arange(9.0).reshape((3, 3))
x2 = np.arange(3.0)
np.divide(2, 4)
np.divide(2, 4.)
np.equal([0, 1, 3], np.arange(3))
np.equal(1, np.ones(1))
x = np.linspace(-2 * np.pi, 2 * np.pi, 100)
np.exp2([2, 3])
np.expm1(1e-10)
np.exp(1e-10) - 1
np.fabs(-1)
np.fabs([-1.2, 1.2])
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.floor(a)
np.floor_divide(7, 3)
np.floor_divide([1., 2., 3., 4.], 2.5)
np.fmod([-3, -2, -1, 1, 2, 3], 2)
np.remainder([-3, -2, -1, 1, 2, 3], 2)
np.fmod([5, 3], [2, 2.])
a = np.arange(-3, 3).reshape(3, 2)
np.fmod(a, [2, 2])
np.greater([4, 2], [2, 2])
a = np.array([4, 2])
b = np.array([2, 2])
a > b
np.greater_equal([4, 2, 1], [2, 2, 2])
np.hypot(3 * np.ones((3, 3)), 4 * np.ones((3, 3)))
np.hypot(3 * np.ones((3, 3)), [4])
np.bitwise_not is np.invert
np.invert(np.array([13], dtype=np.uint8))
# np.binary_repr(242, width=8)
np.invert(np.array([13], dtype=np.uint16))
np.invert(np.array([13], dtype=np.int8))
# np.binary_repr(-14, width=8)
np.invert(np.array([True, False]))
# np.isfinite(1)
# np.isfinite(0)
# np.isfinite(np.nan)
# np.isfinite(np.inf)
# np.isfinite(np.NINF)
x = np.array([-np.inf, 0., np.inf])
y = np.array([2, 2, 2])
np.isfinite(x, y)
# np.isinf(np.inf)
# np.isinf(np.nan)
# np.isinf(np.NINF)
# np.isinf([np.inf, -np.inf, 1.0, np.nan])
x = np.array([-np.inf, 0., np.inf])
y = np.array([2, 2, 2])
# np.isinf(x, y)
# np.isnan(np.nan)
# np.isnan(np.inf)
# np.binary_repr(5)
np.left_shift(5, 2)
# np.binary_repr(20)
np.left_shift(5, [1, 2, 3])
np.less([1, 2], [2, 2])
np.less_equal([4, 2, 1], [2, 2, 2])
x = np.array([0, 1, 2, 2**4])
xi = np.array([0 + 1.j, 1, 2 + 0.j, 4.j])
np.log2(xi)
prob1 = np.log(1e-50)
prob2 = np.log(2.5e-50)
prob12 = np.logaddexp(prob1, prob2)
prob12
np.exp(prob12)
prob1 = np.log2(1e-50)
prob2 = np.log2(2.5e-50)
prob12 = np.logaddexp2(prob1, prob2)
prob1, prob2, prob12
2**prob12
np.log1p(1e-99)
np.log(1 + 1e-99)
# np.logical_and(True, False)
# np.logical_and([True, False], [False, False])
x = np.arange(5)
# np.logical_and(x>1, x<4)
# np.logical_not(3)
# np.logical_not([True, False, 0, 1])
x = np.arange(5)
# np.logical_not(x<3)
# np.logical_or(True, False)
# np.logical_or([True, False], [False, False])
x = np.arange(5)
# np.logical_or(x < 1, x > 3)
# np.logical_xor(True, False)
# np.logical_xor([True, True, False, False], [True, False, True, False])
x = np.arange(5)
# np.logical_xor(x < 1, x > 3)
# np.logical_xor(0, np.eye(2))
np.maximum([2, 3, 4], [1, 5, 2])
# np.maximum([np.nan, 0, np.nan], [0, np.nan, np.nan])
# np.maximum(np.Inf, 1)
np.minimum([2, 3, 4], [1, 5, 2])
# np.minimum([np.nan, 0, np.nan],[0, np.nan, np.nan])
# np.minimum(-np.Inf, 1)
np.fmax([2, 3, 4], [1, 5, 2])
np.fmax(np.eye(2), [0.5, 2])
# np.fmax([np.nan, 0, np.nan],[0, np.nan, np.nan])
np.fmin([2, 3, 4], [1, 5, 2])
np.fmin(np.eye(2), [0.5, 2])
# np.fmin([np.nan, 0, np.nan],[0, np.nan, np.nan])
np.modf([0, 3.5])
np.modf(-0.5)
np.multiply(2.0, 4.0)
x1 = np.arange(9.0).reshape((3, 3))
x2 = np.arange(3.0)
np.multiply(x1, x2)
np.negative([1., -1.])
np.not_equal([1., 2.], [1., 3.])
np.not_equal([1, 2], [[1, 3], [1, 4]])
x1 = range(6)
np.power(x1, 3)
x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0]
np.power(x1, x2)
x2 = np.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
np.power(x1, x2)
deg = np.arange(12.) * 30.
np.radians(deg)
out = np.zeros((deg.shape))
ret = np.radians(deg, out)
ret is out
np.deg2rad(180)
np.reciprocal(2.)
np.reciprocal([1, 2., 3.33])
np.remainder([4, 7], [2, 3])
np.remainder(np.arange(7), 5)
# np.binary_repr(10)
np.right_shift(10, 1)
# np.binary_repr(5)
np.right_shift(10, [1, 2, 3])
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.rint(a)
np.sign([-5., 4.5])
np.sign(0)
# np.sign(5-2j)
# np.signbit(-1.2)
np.signbit(np.array([1, -2.3, 2.1]))
np.copysign(1.3, -1)
np.copysign([-1, 0, 1], -1.1)
np.copysign([-1, 0, 1], np.arange(3) - 1)
np.sin(np.pi / 2.)
np.sin(np.array((0., 30., 45., 60., 90.)) * np.pi / 180.)
x = np.linspace(-np.pi, np.pi, 201)
np.sinh(0)
# np.sinh(np.pi*1j/2)
np.sqrt([1, 4, 9])
np.sqrt([4, -1, -3 + 4J])
np.cbrt([1, 8, 27])
np.square([-1j, 1])
np.subtract(1.0, 4.0)
x1 = np.arange(9.0).reshape((3, 3))
x2 = np.arange(3.0)
np.subtract(x1, x2)
np.tan(np.array([-pi, pi / 2, pi]))
np.tanh((0, np.pi * 1j, np.pi * 1j / 2))
x = np.arange(5)
np.true_divide(x, 4)
x = np.arange(9)
y1, y2 = np.frexp(x)
y1 * 2**y2
np.ldexp(5, np.arange(4))
x = np.arange(6)
np.ldexp(*np.frexp(x))
def test_numeric():
# 'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc',
# 'arange', 'array', 'zeros', 'count_nonzero', 'empty', 'broadcast',
# 'dtype', 'fromstring', 'fromfile', 'frombuffer', 'int_asbuffer',
# 'where', 'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose',
# 'lexsort', 'set_numeric_ops', 'can_cast', 'promote_types',
# 'min_scalar_type', 'result_type', 'asarray', 'asanyarray',
# 'ascontiguousarray', 'asfortranarray', 'isfortran', 'empty_like',
# 'zeros_like', 'ones_like', 'correlate', 'convolve', 'inner', 'dot',
# 'einsum', 'outer', 'vdot', 'alterdot', 'restoredot', 'roll',
# 'rollaxis', 'moveaxis', 'cross', 'tensordot', 'array2string',
# 'get_printoptions', 'set_printoptions', 'array_repr', 'array_str',
# 'set_string_function', 'little_endian', 'require', 'fromiter',
# 'array_equal', 'array_equiv', 'indices', 'fromfunction', 'isclose', 'load',
# 'loads', 'isscalar', 'binary_repr', 'base_repr', 'ones', 'identity',
# 'allclose', 'compare_chararrays', 'putmask', 'seterr', 'geterr',
# 'setbufsize', 'getbufsize', 'seterrcall', 'geterrcall', 'errstate',
# 'flatnonzero', 'Inf', 'inf', 'infty', 'Infinity', 'nan', 'NaN', 'False_',
# 'True_', 'bitwise_not', 'full', 'full_like', 'matmul'
x = np.arange(6)
x = x.reshape((2, 3))
np.zeros_like(x)
y = np.arange(3, dtype=np.float)
np.zeros_like(y)
np.ones(5)
np.ones((5, ), dtype=np.int)
np.ones((2, 1))
s = (2, 2)
np.ones(s)
x = np.arange(6)
x = x.reshape((2, 3))
np.ones_like(x)
y = np.arange(3, dtype=np.float)
np.ones_like(y)
np.full((2, 2), np.inf)
x = np.arange(6, dtype=np.int)
np.full_like(x, 1)
np.full_like(x, 0.1)
np.full_like(y, 0.1)
np.count_nonzero(np.eye(4))
np.count_nonzero([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]])
np.count_nonzero([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]], axis=0)
np.count_nonzero([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]], axis=1)
a = [1, 2]
np.asarray(a)
a = np.array([1, 2])
np.asarray(a) is a
a = np.array([1, 2], dtype=np.float32)
np.asarray(a, dtype=np.float32) is a
np.asarray(a, dtype=np.float64) is a
np.asarray(a) is a
np.asanyarray(a) is a
a = [1, 2]
np.asanyarray(a)
np.asanyarray(a) is a
x = np.arange(6).reshape(2, 3)
np.ascontiguousarray(x, dtype=np.float32)
x = np.arange(6).reshape(2, 3)
y = np.asfortranarray(x)
x = np.arange(6).reshape(2, 3)
y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
np.isfortran(a)
b = np.array([[1, 2, 3], [4, 5, 6]], order='FORTRAN')
np.isfortran(b)
a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
np.isfortran(a)
b = a.T
np.isfortran(b)
np.isfortran(np.array([1, 2], order='FORTRAN'))
x = np.arange(6).reshape(2, 3)
np.argwhere(x > 1)
x = np.arange(-2, 3)
np.flatnonzero(x)
np.correlate([1, 2, 3], [0, 1, 0.5])
np.correlate([1, 2, 3], [0, 1, 0.5], "same")
np.correlate([1, 2, 3], [0, 1, 0.5], "full")
np.correlate([1 + 1j, 2, 3 - 1j], [0, 1, 0.5j], 'full')
np.correlate([0, 1, 0.5j], [1 + 1j, 2, 3 - 1j], 'full')
np.convolve([1, 2, 3], [0, 1, 0.5])
np.convolve([1, 2, 3], [0, 1, 0.5], 'same')
np.convolve([1, 2, 3], [0, 1, 0.5], 'valid')
rl = np.outer(np.ones((5, )), np.linspace(-2, 2, 5))
# im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))
# grid = rl + im
x = np.array(['a', 'b', 'c'], dtype=object)
np.outer(x, [1, 2, 3])
a = np.arange(60.).reshape(3, 4, 5)
b = np.arange(24.).reshape(4, 3, 2)
c = np.tensordot(a, b, axes=([1, 0], [0, 1]))
c.shape
# A slower but equivalent way of computing the same...
d = np.zeros((5, 2))
a = np.array(range(1, 9))
A = np.array(('a', 'b', 'c', 'd'), dtype=object)
x = np.arange(10)
np.roll(x, 2)
x2 = np.reshape(x, (2, 5))
np.roll(x2, 1)
np.roll(x2, 1, axis=0)
np.roll(x2, 1, axis=1)
a = np.ones((3, 4, 5, 6))
np.rollaxis(a, 3, 1).shape
np.rollaxis(a, 2).shape
np.rollaxis(a, 1, 4).shape
x = np.zeros((3, 4, 5))
np.moveaxis(x, 0, -1).shape
np.moveaxis(x, -1, 0).shape
np.transpose(x).shape
np.moveaxis(x, [0, 1], [-1, -2]).shape
np.moveaxis(x, [0, 1, 2], [-1, -2, -3]).shape
x = [1, 2, 3]
y = [4, 5, 6]
np.cross(x, y)
x = [1, 2]
y = [4, 5, 6]
np.cross(x, y)
x = [1, 2, 0]
y = [4, 5, 6]
np.cross(x, y)
x = [1, 2]
y = [4, 5]
np.cross(x, y)
x = np.array([[1, 2, 3], [4, 5, 6]])
y = np.array([[4, 5, 6], [1, 2, 3]])
np.cross(x, y)
np.cross(x, y, axisc=0)
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([[7, 8, 9], [4, 5, 6], [1, 2, 3]])
np.cross(x, y)
np.cross(x, y, axisa=0, axisb=0)
# np.array_repr(np.array([1,2]))
# np.array_repr(np.ma.array([0.]))
# np.array_repr(np.array([], np.int32))
x = np.array([1e-6, 4e-7, 2, 3])
# np.array_repr(x, precision=6, suppress_small=True)
# np.array_str(np.arange(3))
a = np.arange(10)
x = np.arange(4)
np.set_string_function(lambda x: 'random', repr=False)
grid = np.indices((2, 3))
grid.shape
grid[0] # row indices
grid[1] # column indices
x = np.arange(20).reshape(5, 4)
row, col = np.indices((2, 3))
x[row, col]
np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)
np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)
np.isscalar(3.1)
np.isscalar([3.1])
np.isscalar(False)
# np.binary_repr(3)
# np.binary_repr(-3)
# np.binary_repr(3, width=4)
# np.binary_repr(-3, width=3)
# np.binary_repr(-3, width=5)
# np.base_repr(5)
# np.base_repr(6, 5)
# np.base_repr(7, base=5, padding=3)
# np.base_repr(10, base=16)
# np.base_repr(32, base=16)
np.identity(3)
np.allclose([1e10, 1e-7], [1.00001e10, 1e-8])
np.allclose([1e10, 1e-8], [1.00001e10, 1e-9])
np.allclose([1e10, 1e-8], [1.0001e10, 1e-9])
# np.allclose([1.0, np.nan], [1.0, np.nan])
# np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
np.isclose([1e10, 1e-7], [1.00001e10, 1e-8])
np.isclose([1e10, 1e-8], [1.00001e10, 1e-9])
np.isclose([1e10, 1e-8], [1.0001e10, 1e-9])
# np.isclose([1.0, np.nan], [1.0, np.nan])
# np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
np.array_equal([1, 2], [1, 2])
np.array_equal(np.array([1, 2]), np.array([1, 2]))
np.array_equal([1, 2], [1, 2, 3])
np.array_equal([1, 2], [1, 4])
np.array_equiv([1, 2], [1, 2])
np.array_equiv([1, 2], [1, 3])
np.array_equiv([1, 2], [[1, 2], [1, 2]])
np.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]])
np.array_equiv([1, 2], [[1, 2], [1, 3]])
def test_numeric():
# 'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc',
# 'arange', 'array', 'zeros', 'count_nonzero', 'empty', 'broadcast',
# 'dtype', 'fromstring', 'fromfile', 'frombuffer', 'int_asbuffer',
# 'where', 'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose',
# 'lexsort', 'set_numeric_ops', 'can_cast', 'promote_types',
# 'min_scalar_type', 'result_type', 'asarray', 'asanyarray',
# 'ascontiguousarray', 'asfortranarray', 'isfortran', 'empty_like',
# 'zeros_like', 'ones_like', 'correlate', 'convolve', 'inner', 'dot',
# 'einsum', 'outer', 'vdot', 'alterdot', 'restoredot', 'roll',
# 'rollaxis', 'moveaxis', 'cross', 'tensordot', 'array2string',
# 'get_printoptions', 'set_printoptions', 'array_repr', 'array_str',
# 'set_string_function', 'little_endian', 'require', 'fromiter',
# 'array_equal', 'array_equiv', 'indices', 'fromfunction', 'isclose', 'load',
# 'loads', 'isscalar', 'binary_repr', 'base_repr', 'ones', 'identity',
# 'allclose', 'compare_chararrays', 'putmask', 'seterr', 'geterr',
# 'setbufsize', 'getbufsize', 'seterrcall', 'geterrcall', 'errstate',
# 'flatnonzero', 'Inf', 'inf', 'infty', 'Infinity', 'nan', 'NaN', 'False_',
# 'True_', 'bitwise_not', 'full', 'full_like', 'matmul'
x = np.arange(6)
x = x.reshape((2, 3))
np.zeros_like(x)
y = np.arange(3, dtype=np.float)
np.zeros_like(y)
np.ones(5)
np.ones((5,), dtype=np.int)
np.ones((2, 1))
s = (2,2)
np.ones(s)
x = np.arange(6)
x = x.reshape((2, 3))
np.ones_like(x)
y = np.arange(3, dtype=np.float)
np.ones_like(y)
np.full((2, 2), np.inf)
x = np.arange(6, dtype=np.int)
np.full_like(x, 1)
np.full_like(x, 0.1)
np.full_like(y, 0.1)
np.count_nonzero(np.eye(4))
np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]])
np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]], axis=0)
np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]], axis=1)
a = [1, 2]
np.asarray(a)
a = np.array([1, 2])
np.asarray(a) is a
a = np.array([1, 2], dtype=np.float32)
np.asarray(a, dtype=np.float32) is a
np.asarray(a, dtype=np.float64) is a
np.asarray(a) is a
np.asanyarray(a) is a
a = [1, 2]
np.asanyarray(a)
np.asanyarray(a) is a
x = np.arange(6).reshape(2,3)
np.ascontiguousarray(x, dtype=np.float32)
x = np.arange(6).reshape(2,3)
y = np.asfortranarray(x)
x = np.arange(6).reshape(2,3)
y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
np.isfortran(a)
b = np.array([[1, 2, 3], [4, 5, 6]], order='FORTRAN')
np.isfortran(b)
a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
np.isfortran(a)
b = a.T
np.isfortran(b)
np.isfortran(np.array([1, 2], order='FORTRAN'))
x = np.arange(6).reshape(2,3)
np.argwhere(x>1)
x = np.arange(-2, 3)
np.flatnonzero(x)
np.correlate([1, 2, 3], [0, 1, 0.5])
np.correlate([1, 2, 3], [0, 1, 0.5], "same")
np.correlate([1, 2, 3], [0, 1, 0.5], "full")
np.correlate([1+1j, 2, 3-1j], [0, 1, 0.5j], 'full')
np.correlate([0, 1, 0.5j], [1+1j, 2, 3-1j], 'full')
np.convolve([1, 2, 3], [0, 1, 0.5])
np.convolve([1,2,3],[0,1,0.5], 'same')
np.convolve([1,2,3],[0,1,0.5], 'valid')
rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))
# im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))
# grid = rl + im
x = np.array(['a', 'b', 'c'], dtype=object)
np.outer(x, [1, 2, 3])
a = np.arange(60.).reshape(3,4,5)
b = np.arange(24.).reshape(4,3,2)
c = np.tensordot(a,b, axes=([1,0],[0,1]))
c.shape
# A slower but equivalent way of computing the same...
d = np.zeros((5,2))
a = np.array(range(1, 9))
A = np.array(('a', 'b', 'c', 'd'), dtype=object)
x = np.arange(10)
np.roll(x, 2)
x2 = np.reshape(x, (2,5))
np.roll(x2, 1)
np.roll(x2, 1, axis=0)
np.roll(x2, 1, axis=1)
a = np.ones((3,4,5,6))
np.rollaxis(a, 3, 1).shape
np.rollaxis(a, 2).shape
np.rollaxis(a, 1, 4).shape
x = np.zeros((3, 4, 5))
np.moveaxis(x, 0, -1).shape
np.moveaxis(x, -1, 0).shape
np.transpose(x).shape
np.moveaxis(x, [0, 1], [-1, -2]).shape
np.moveaxis(x, [0, 1, 2], [-1, -2, -3]).shape
x = [1, 2, 3]
y = [4, 5, 6]
np.cross(x, y)
x = [1, 2]
y = [4, 5, 6]
np.cross(x, y)
x = [1, 2, 0]
y = [4, 5, 6]
np.cross(x, y)
x = [1,2]
y = [4,5]
np.cross(x, y)
x = np.array([[1,2,3], [4,5,6]])
y = np.array([[4,5,6], [1,2,3]])
np.cross(x, y)
np.cross(x, y, axisc=0)
x = np.array([[1,2,3], [4,5,6], [7, 8, 9]])
y = np.array([[7, 8, 9], [4,5,6], [1,2,3]])
np.cross(x, y)
np.cross(x, y, axisa=0, axisb=0)
# np.array_repr(np.array([1,2]))
# np.array_repr(np.ma.array([0.]))
# np.array_repr(np.array([], np.int32))
x = np.array([1e-6, 4e-7, 2, 3])
# np.array_repr(x, precision=6, suppress_small=True)
# np.array_str(np.arange(3))
a = np.arange(10)
x = np.arange(4)
np.set_string_function(lambda x:'random', repr=False)
grid = np.indices((2, 3))
grid.shape
grid[0] # row indices
grid[1] # column indices
x = np.arange(20).reshape(5, 4)
row, col = np.indices((2, 3))
x[row, col]
np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)
np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)
np.isscalar(3.1)
np.isscalar([3.1])
np.isscalar(False)
# np.binary_repr(3)
# np.binary_repr(-3)
# np.binary_repr(3, width=4)
# np.binary_repr(-3, width=3)
# np.binary_repr(-3, width=5)
# np.base_repr(5)
# np.base_repr(6, 5)
# np.base_repr(7, base=5, padding=3)
# np.base_repr(10, base=16)
# np.base_repr(32, base=16)
np.identity(3)
np.allclose([1e10,1e-7], [1.00001e10,1e-8])
np.allclose([1e10,1e-8], [1.00001e10,1e-9])
np.allclose([1e10,1e-8], [1.0001e10,1e-9])
# np.allclose([1.0, np.nan], [1.0, np.nan])
# np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
np.isclose([1e10,1e-7], [1.00001e10,1e-8])
np.isclose([1e10,1e-8], [1.00001e10,1e-9])
np.isclose([1e10,1e-8], [1.0001e10,1e-9])
# np.isclose([1.0, np.nan], [1.0, np.nan])
# np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
np.array_equal([1, 2], [1, 2])
np.array_equal(np.array([1, 2]), np.array([1, 2]))
np.array_equal([1, 2], [1, 2, 3])
np.array_equal([1, 2], [1, 4])
np.array_equiv([1, 2], [1, 2])
np.array_equiv([1, 2], [1, 3])
np.array_equiv([1, 2], [[1, 2], [1, 2]])
np.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]])
np.array_equiv([1, 2], [[1, 2], [1, 3]])
def test_ufunc():
x = np.array([-1.2, 1.2])
np.absolute(x)
np.absolute(1.2 + 1j)
x = np.linspace(start=-10, stop=10, num=101)
np.add(1.0, 4.0)
x1 = np.arange(9.0).reshape((3, 3))
x2 = np.arange(3.0)
np.add(x1, x2)
np.arccos([1, -1])
x = np.linspace(-1, 1, num=100)
np.arccosh([np.e, 10.0])
np.arccosh(1)
np.arcsin(0)
np.arcsinh(np.array([np.e, 10.0]))
np.arctan([0, 1])
np.pi/4
x = np.linspace(-10, 10)
x = np.array([-1, +1, +1, -1])
y = np.array([-1, -1, +1, +1])
np.arctan2(y, x) * 180 / np.pi
np.arctan2([1., -1.], [0., 0.])
np.arctan2([0., 0., np.inf], [+0., -0., np.inf])
np.arctanh([0, -0.5])
np.bitwise_and(13, 17)
np.bitwise_and(14, 13)
# np.binary_repr(12) return str
np.bitwise_and([14,3], 13)
np.bitwise_and([11,7], [4,25])
np.bitwise_and(np.array([2,5,255]), np.array([3,14,16]))
np.bitwise_and([True, True], [False, True])
np.bitwise_or(13, 16)
# np.binary_repr(29)
np.bitwise_or(32, 2)
np.bitwise_or([33, 4], 1)
np.bitwise_or([33, 4], [1, 2])
np.bitwise_or(np.array([2, 5, 255]), np.array([4, 4, 4]))
# np.array([2, 5, 255]) | np.array([4, 4, 4])
np.bitwise_or(np.array([2, 5, 255, 2147483647], dtype=np.int32),
np.array([4, 4, 4, 2147483647], dtype=np.int32))
np.bitwise_or([True, True], [False, True])
np.bitwise_xor(13, 17)
# np.binary_repr(28)
np.bitwise_xor(31, 5)
np.bitwise_xor([31,3], 5)
np.bitwise_xor([31,3], [5,6])
np.bitwise_xor([True, True], [False, True])
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.ceil(a)
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.trunc(a)
np.cos(np.array([0, np.pi/2, np.pi]))
np.cosh(0)
x = np.linspace(-4, 4, 1000)
rad = np.arange(12.)*np.pi/6
np.degrees(rad)
out = np.zeros((rad.shape))
r = np.degrees(rad, out)
# np.all(r == out) return bool
np.rad2deg(np.pi/2)
np.divide(2.0, 4.0)
x1 = np.arange(9.0).reshape((3, 3))
x2 = np.arange(3.0)
np.divide(2, 4)
np.divide(2, 4.)
np.equal([0, 1, 3], np.arange(3))
np.equal(1, np.ones(1))
x = np.linspace(-2*np.pi, 2*np.pi, 100)
np.exp2([2, 3])
np.expm1(1e-10)
np.exp(1e-10) - 1
np.fabs(-1)
np.fabs([-1.2, 1.2])
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.floor(a)
np.floor_divide(7,3)
np.floor_divide([1., 2., 3., 4.], 2.5)
np.fmod([-3, -2, -1, 1, 2, 3], 2)
np.remainder([-3, -2, -1, 1, 2, 3], 2)
np.fmod([5, 3], [2, 2.])
a = np.arange(-3, 3).reshape(3, 2)
np.fmod(a, [2,2])
np.greater([4,2],[2,2])
a = np.array([4,2])
b = np.array([2,2])
a > b
np.greater_equal([4, 2, 1], [2, 2, 2])
np.hypot(3*np.ones((3, 3)), 4*np.ones((3, 3)))
np.hypot(3*np.ones((3, 3)), [4])
np.bitwise_not is np.invert
np.invert(np.array([13], dtype=np.uint8))
# np.binary_repr(242, width=8)
np.invert(np.array([13], dtype=np.uint16))
np.invert(np.array([13], dtype=np.int8))
# np.binary_repr(-14, width=8)
np.invert(np.array([True, False]))
# np.isfinite(1)
# np.isfinite(0)
# np.isfinite(np.nan)
# np.isfinite(np.inf)
# np.isfinite(np.NINF)
x = np.array([-np.inf, 0., np.inf])
y = np.array([2, 2, 2])
np.isfinite(x, y)
# np.isinf(np.inf)
# np.isinf(np.nan)
# np.isinf(np.NINF)
# np.isinf([np.inf, -np.inf, 1.0, np.nan])
x = np.array([-np.inf, 0., np.inf])
y = np.array([2, 2, 2])
# np.isinf(x, y)
# np.isnan(np.nan)
# np.isnan(np.inf)
# np.binary_repr(5)
np.left_shift(5, 2)
# np.binary_repr(20)
np.left_shift(5, [1,2,3])
np.less([1, 2], [2, 2])
np.less_equal([4, 2, 1], [2, 2, 2])
x = np.array([0, 1, 2, 2**4])
xi = np.array([0+1.j, 1, 2+0.j, 4.j])
np.log2(xi)
prob1 = np.log(1e-50)
prob2 = np.log(2.5e-50)
prob12 = np.logaddexp(prob1, prob2)
prob12
np.exp(prob12)
prob1 = np.log2(1e-50)
prob2 = np.log2(2.5e-50)
prob12 = np.logaddexp2(prob1, prob2)
prob1, prob2, prob12
2**prob12
np.log1p(1e-99)
np.log(1 + 1e-99)
# np.logical_and(True, False)
# np.logical_and([True, False], [False, False])
x = np.arange(5)
# np.logical_and(x>1, x<4)
# np.logical_not(3)
# np.logical_not([True, False, 0, 1])
x = np.arange(5)
# np.logical_not(x<3)
# np.logical_or(True, False)
# np.logical_or([True, False], [False, False])
x = np.arange(5)
# np.logical_or(x < 1, x > 3)
# np.logical_xor(True, False)
# np.logical_xor([True, True, False, False], [True, False, True, False])
x = np.arange(5)
# np.logical_xor(x < 1, x > 3)
# np.logical_xor(0, np.eye(2))
np.maximum([2, 3, 4], [1, 5, 2])
# np.maximum([np.nan, 0, np.nan], [0, np.nan, np.nan])
# np.maximum(np.Inf, 1)
np.minimum([2, 3, 4], [1, 5, 2])
# np.minimum([np.nan, 0, np.nan],[0, np.nan, np.nan])
# np.minimum(-np.Inf, 1)
np.fmax([2, 3, 4], [1, 5, 2])
np.fmax(np.eye(2), [0.5, 2])
# np.fmax([np.nan, 0, np.nan],[0, np.nan, np.nan])
np.fmin([2, 3, 4], [1, 5, 2])
np.fmin(np.eye(2), [0.5, 2])
# np.fmin([np.nan, 0, np.nan],[0, np.nan, np.nan])
np.modf([0, 3.5])
np.modf(-0.5)
np.multiply(2.0, 4.0)
x1 = np.arange(9.0).reshape((3, 3))
x2 = np.arange(3.0)
np.multiply(x1, x2)
np.negative([1.,-1.])
np.not_equal([1.,2.], [1., 3.])
np.not_equal([1, 2], [[1, 3],[1, 4]])
x1 = range(6)
np.power(x1, 3)
x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0]
np.power(x1, x2)
x2 = np.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
np.power(x1, x2)
deg = np.arange(12.) * 30.
np.radians(deg)
out = np.zeros((deg.shape))
ret = np.radians(deg, out)
ret is out
np.deg2rad(180)
np.reciprocal(2.)
np.reciprocal([1, 2., 3.33])
np.remainder([4, 7], [2, 3])
np.remainder(np.arange(7), 5)
# np.binary_repr(10)
np.right_shift(10, 1)
# np.binary_repr(5)
np.right_shift(10, [1,2,3])
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.rint(a)
np.sign([-5., 4.5])
np.sign(0)
# np.sign(5-2j)
# np.signbit(-1.2)
np.signbit(np.array([1, -2.3, 2.1]))
np.copysign(1.3, -1)
np.copysign([-1, 0, 1], -1.1)
np.copysign([-1, 0, 1], np.arange(3)-1)
np.sin(np.pi/2.)
np.sin(np.array((0., 30., 45., 60., 90.)) * np.pi / 180. )
x = np.linspace(-np.pi, np.pi, 201)
np.sinh(0)
# np.sinh(np.pi*1j/2)
np.sqrt([1,4,9])
np.sqrt([4, -1, -3+4J])
np.cbrt([1,8,27])
np.square([-1j, 1])
np.subtract(1.0, 4.0)
x1 = np.arange(9.0).reshape((3, 3))
x2 = np.arange(3.0)
np.subtract(x1, x2)
np.tan(np.array([-pi,pi/2,pi]))
np.tanh((0, np.pi*1j, np.pi*1j/2))
x = np.arange(5)
np.true_divide(x, 4)
x = np.arange(9)
y1, y2 = np.frexp(x)
y1 * 2**y2
np.ldexp(5, np.arange(4))
x = np.arange(6)
np.ldexp(*np.frexp(x))