Python numpy.fmax() Examples
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Example #1
Source File: test_ufunc.py From pySINDy with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #2
Source File: test_umath.py From recruit with Apache License 2.0 | 6 votes |
def test_reduce(self): dflt = np.typecodes['AllFloat'] dint = np.typecodes['AllInteger'] seq1 = np.arange(11) seq2 = seq1[::-1] func = np.fmax.reduce for dt in dint: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) for dt in dflt: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) tmp1[::2] = np.nan tmp2[::2] = np.nan assert_equal(func(tmp1), 9) assert_equal(func(tmp2), 9)
Example #3
Source File: test_ufunc.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod, np.greater, np.greater_equal, np.less, np.less_equal, np.equal, np.not_equal] a = np.array('1') b = 1 c = np.array([1., 2.]) for f in binary_funcs: assert_raises(TypeError, f, a, b) assert_raises(TypeError, f, c, a)
Example #4
Source File: test_ufunc.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #5
Source File: test_umath.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def test_reduce(self): dflt = np.typecodes['AllFloat'] dint = np.typecodes['AllInteger'] seq1 = np.arange(11) seq2 = seq1[::-1] func = np.fmax.reduce for dt in dint: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) for dt in dflt: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) tmp1[::2] = np.nan tmp2[::2] = np.nan assert_equal(func(tmp1), 9) assert_equal(func(tmp2), 9)
Example #6
Source File: test_ufunc.py From vnpy_crypto with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #7
Source File: test_umath.py From vnpy_crypto with MIT License | 6 votes |
def test_reduce(self): dflt = np.typecodes['AllFloat'] dint = np.typecodes['AllInteger'] seq1 = np.arange(11) seq2 = seq1[::-1] func = np.fmax.reduce for dt in dint: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) for dt in dflt: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) tmp1[::2] = np.nan tmp2[::2] = np.nan assert_equal(func(tmp1), 9) assert_equal(func(tmp2), 9)
Example #8
Source File: test_umath.py From Computable with MIT License | 6 votes |
def test_reduce(self): dflt = np.typecodes['AllFloat'] dint = np.typecodes['AllInteger'] seq1 = np.arange(11) seq2 = seq1[::-1] func = np.fmax.reduce for dt in dint: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) for dt in dflt: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) tmp1[::2] = np.nan tmp2[::2] = np.nan assert_equal(func(tmp1), 9) assert_equal(func(tmp2), 9)
Example #9
Source File: test_umath.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def test_reduce(self): dflt = np.typecodes['AllFloat'] dint = np.typecodes['AllInteger'] seq1 = np.arange(11) seq2 = seq1[::-1] func = np.fmax.reduce for dt in dint: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) for dt in dflt: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) tmp1[::2] = np.nan tmp2[::2] = np.nan assert_equal(func(tmp1), 9) assert_equal(func(tmp2), 9)
Example #10
Source File: test_ufunc.py From GraphicDesignPatternByPython with MIT License | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
Example #11
Source File: test_umath.py From GraphicDesignPatternByPython with MIT License | 6 votes |
def test_reduce(self): dflt = np.typecodes['AllFloat'] dint = np.typecodes['AllInteger'] seq1 = np.arange(11) seq2 = seq1[::-1] func = np.fmax.reduce for dt in dint: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) for dt in dflt: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) tmp1[::2] = np.nan tmp2[::2] = np.nan assert_equal(func(tmp1), 9) assert_equal(func(tmp2), 9)
Example #12
Source File: test_ufunc.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod, np.greater, np.greater_equal, np.less, np.less_equal, np.equal, np.not_equal] a = np.array('1') b = 1 c = np.array([1., 2.]) for f in binary_funcs: assert_raises(TypeError, f, a, b) assert_raises(TypeError, f, c, a)
Example #13
Source File: test_umath.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def test_reduce(self): dflt = np.typecodes['AllFloat'] dint = np.typecodes['AllInteger'] seq1 = np.arange(11) seq2 = seq1[::-1] func = np.fmax.reduce for dt in dint: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) for dt in dflt: tmp1 = seq1.astype(dt) tmp2 = seq2.astype(dt) assert_equal(func(tmp1), 10) assert_equal(func(tmp2), 10) tmp1[::2] = np.nan tmp2[::2] = np.nan assert_equal(func(tmp1), 9) assert_equal(func(tmp2), 9)
Example #14
Source File: np_box_list_ops.py From vehicle_counting_tensorflow with MIT License | 5 votes |
def clip_to_window(boxlist, window): """Clip bounding boxes to a window. This op clips input bounding boxes (represented by bounding box corners) to a window, optionally filtering out boxes that do not overlap at all with the window. Args: boxlist: BoxList holding M_in boxes window: a numpy array of shape [4] representing the [y_min, x_min, y_max, x_max] window to which the op should clip boxes. Returns: a BoxList holding M_out boxes where M_out <= M_in """ y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1) win_y_min = window[0] win_x_min = window[1] win_y_max = window[2] win_x_max = window[3] y_min_clipped = np.fmax(np.fmin(y_min, win_y_max), win_y_min) y_max_clipped = np.fmax(np.fmin(y_max, win_y_max), win_y_min) x_min_clipped = np.fmax(np.fmin(x_min, win_x_max), win_x_min) x_max_clipped = np.fmax(np.fmin(x_max, win_x_max), win_x_min) clipped = np_box_list.BoxList( np.hstack([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped])) clipped = _copy_extra_fields(clipped, boxlist) areas = area(clipped) nonzero_area_indices = np.reshape(np.nonzero(np.greater(areas, 0.0)), [-1]).astype(np.int32) return gather(clipped, nonzero_area_indices)
Example #15
Source File: unittest.py From overhaul-distillation with MIT License | 5 votes |
def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3): npa, npb = as_numpy(a), as_numpy(b) self.assertTrue( np.allclose(npa, npb, atol=atol), 'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max()) )
Example #16
Source File: test_umath.py From GraphicDesignPatternByPython with MIT License | 5 votes |
def test_reduce_complex(self): assert_equal(np.fmax.reduce([1, 2j]), 1) assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)
Example #17
Source File: test_ufunc.py From recruit with Apache License 2.0 | 5 votes |
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod, np.greater, np.greater_equal, np.less, np.less_equal, np.equal, np.not_equal] a = np.array('1') b = 1 c = np.array([1., 2.]) for f in binary_funcs: assert_raises(TypeError, f, a, b) assert_raises(TypeError, f, c, a)
Example #18
Source File: speechprocessing.py From project-CURRENNT-scripts with BSD 3-Clause "New" or "Revised" License | 5 votes |
def _amplitude(self, Y): eps = 1.0E-12 A = np.fmax(np.absolute(Y), eps) return A
Example #19
Source File: np_box_list_ops.py From tensorflow with BSD 2-Clause "Simplified" License | 5 votes |
def clip_to_window(boxlist, window): """Clip bounding boxes to a window. This op clips input bounding boxes (represented by bounding box corners) to a window, optionally filtering out boxes that do not overlap at all with the window. Args: boxlist: BoxList holding M_in boxes window: a numpy array of shape [4] representing the [y_min, x_min, y_max, x_max] window to which the op should clip boxes. Returns: a BoxList holding M_out boxes where M_out <= M_in """ y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1) win_y_min = window[0] win_x_min = window[1] win_y_max = window[2] win_x_max = window[3] y_min_clipped = np.fmax(np.fmin(y_min, win_y_max), win_y_min) y_max_clipped = np.fmax(np.fmin(y_max, win_y_max), win_y_min) x_min_clipped = np.fmax(np.fmin(x_min, win_x_max), win_x_min) x_max_clipped = np.fmax(np.fmin(x_max, win_x_max), win_x_min) clipped = np_box_list.BoxList( np.hstack([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped])) clipped = _copy_extra_fields(clipped, boxlist) areas = area(clipped) nonzero_area_indices = np.reshape(np.nonzero(np.greater(areas, 0.0)), [-1]).astype(np.int32) return gather(clipped, nonzero_area_indices)
Example #20
Source File: np_box_list_ops.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 5 votes |
def clip_to_window(boxlist, window): """Clip bounding boxes to a window. This op clips input bounding boxes (represented by bounding box corners) to a window, optionally filtering out boxes that do not overlap at all with the window. Args: boxlist: BoxList holding M_in boxes window: a numpy array of shape [4] representing the [y_min, x_min, y_max, x_max] window to which the op should clip boxes. Returns: a BoxList holding M_out boxes where M_out <= M_in """ y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1) win_y_min = window[0] win_x_min = window[1] win_y_max = window[2] win_x_max = window[3] y_min_clipped = np.fmax(np.fmin(y_min, win_y_max), win_y_min) y_max_clipped = np.fmax(np.fmin(y_max, win_y_max), win_y_min) x_min_clipped = np.fmax(np.fmin(x_min, win_x_max), win_x_min) x_max_clipped = np.fmax(np.fmin(x_max, win_x_max), win_x_min) clipped = np_box_list.BoxList( np.hstack([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped])) clipped = _copy_extra_fields(clipped, boxlist) areas = area(clipped) nonzero_area_indices = np.reshape(np.nonzero(np.greater(areas, 0.0)), [-1]).astype(np.int32) return gather(clipped, nonzero_area_indices)
Example #21
Source File: softplus.py From chainer with MIT License | 5 votes |
def forward_cpu(self, inputs): self.retain_inputs((0,)) x = inputs[0] # y = log(1 + exp(beta * x)) / beta bx = self.beta * x y = (numpy.fmax(bx, 0) + numpy.log1p(numpy.exp(-numpy.fabs(bx)))) * self.beta_inv return utils.force_array(y, x.dtype),
Example #22
Source File: test_activation.py From chainer with MIT License | 5 votes |
def func(self, xp, a): in_dtype, = self.in_dtypes if xp is numpy: ba = self.beta * a beta_inv = 1.0 / self.beta y = (numpy.fmax(ba, 0) + numpy.log1p(numpy.exp(-numpy.fabs(ba)))) * beta_inv return y elif self.beta_range is Unspecified: return xp.softplus(a) else: return xp.softplus(a, self.beta)
Example #23
Source File: np_box_list_ops.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 votes |
def clip_to_window(boxlist, window): """Clip bounding boxes to a window. This op clips input bounding boxes (represented by bounding box corners) to a window, optionally filtering out boxes that do not overlap at all with the window. Args: boxlist: BoxList holding M_in boxes window: a numpy array of shape [4] representing the [y_min, x_min, y_max, x_max] window to which the op should clip boxes. Returns: a BoxList holding M_out boxes where M_out <= M_in """ y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1) win_y_min = window[0] win_x_min = window[1] win_y_max = window[2] win_x_max = window[3] y_min_clipped = np.fmax(np.fmin(y_min, win_y_max), win_y_min) y_max_clipped = np.fmax(np.fmin(y_max, win_y_max), win_y_min) x_min_clipped = np.fmax(np.fmin(x_min, win_x_max), win_x_min) x_max_clipped = np.fmax(np.fmin(x_max, win_x_max), win_x_min) clipped = np_box_list.BoxList( np.hstack([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped])) clipped = _copy_extra_fields(clipped, boxlist) areas = area(clipped) nonzero_area_indices = np.reshape(np.nonzero(np.greater(areas, 0.0)), [-1]).astype(np.int32) return gather(clipped, nonzero_area_indices)
Example #24
Source File: np_box_list_ops.py From yolo_v2 with Apache License 2.0 | 5 votes |
def clip_to_window(boxlist, window): """Clip bounding boxes to a window. This op clips input bounding boxes (represented by bounding box corners) to a window, optionally filtering out boxes that do not overlap at all with the window. Args: boxlist: BoxList holding M_in boxes window: a numpy array of shape [4] representing the [y_min, x_min, y_max, x_max] window to which the op should clip boxes. Returns: a BoxList holding M_out boxes where M_out <= M_in """ y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1) win_y_min = window[0] win_x_min = window[1] win_y_max = window[2] win_x_max = window[3] y_min_clipped = np.fmax(np.fmin(y_min, win_y_max), win_y_min) y_max_clipped = np.fmax(np.fmin(y_max, win_y_max), win_y_min) x_min_clipped = np.fmax(np.fmin(x_min, win_x_max), win_x_min) x_max_clipped = np.fmax(np.fmin(x_max, win_x_max), win_x_min) clipped = np_box_list.BoxList( np.hstack([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped])) clipped = _copy_extra_fields(clipped, boxlist) areas = area(clipped) nonzero_area_indices = np.reshape(np.nonzero(np.greater(areas, 0.0)), [-1]).astype(np.int32) return gather(clipped, nonzero_area_indices)
Example #25
Source File: np_box_list_ops.py From HereIsWally with MIT License | 5 votes |
def clip_to_window(boxlist, window): """Clip bounding boxes to a window. This op clips input bounding boxes (represented by bounding box corners) to a window, optionally filtering out boxes that do not overlap at all with the window. Args: boxlist: BoxList holding M_in boxes window: a numpy array of shape [4] representing the [y_min, x_min, y_max, x_max] window to which the op should clip boxes. Returns: a BoxList holding M_out boxes where M_out <= M_in """ y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1) win_y_min = window[0] win_x_min = window[1] win_y_max = window[2] win_x_max = window[3] y_min_clipped = np.fmax(np.fmin(y_min, win_y_max), win_y_min) y_max_clipped = np.fmax(np.fmin(y_max, win_y_max), win_y_min) x_min_clipped = np.fmax(np.fmin(x_min, win_x_max), win_x_min) x_max_clipped = np.fmax(np.fmin(x_max, win_x_max), win_x_min) clipped = np_box_list.BoxList( np.hstack([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped])) clipped = _copy_extra_fields(clipped, boxlist) areas = area(clipped) nonzero_area_indices = np.reshape(np.nonzero(np.greater(areas, 0.0)), [-1]).astype(np.int32) return gather(clipped, nonzero_area_indices)
Example #26
Source File: np_box_list_ops.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
def clip_to_window(boxlist, window): """Clip bounding boxes to a window. This op clips input bounding boxes (represented by bounding box corners) to a window, optionally filtering out boxes that do not overlap at all with the window. Args: boxlist: BoxList holding M_in boxes window: a numpy array of shape [4] representing the [y_min, x_min, y_max, x_max] window to which the op should clip boxes. Returns: a BoxList holding M_out boxes where M_out <= M_in """ y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1) win_y_min = window[0] win_x_min = window[1] win_y_max = window[2] win_x_max = window[3] y_min_clipped = np.fmax(np.fmin(y_min, win_y_max), win_y_min) y_max_clipped = np.fmax(np.fmin(y_max, win_y_max), win_y_min) x_min_clipped = np.fmax(np.fmin(x_min, win_x_max), win_x_min) x_max_clipped = np.fmax(np.fmin(x_max, win_x_max), win_x_min) clipped = np_box_list.BoxList( np.hstack([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped])) clipped = _copy_extra_fields(clipped, boxlist) areas = area(clipped) nonzero_area_indices = np.reshape(np.nonzero(np.greater(areas, 0.0)), [-1]).astype(np.int32) return gather(clipped, nonzero_area_indices)
Example #27
Source File: es.py From NiaPy with MIT License | 5 votes |
def CovarianceMaatrixAdaptionEvolutionStrategyF(task, epsilon=1e-20, rnd=rand): lam, alpha_mu, hs, sigma0 = (4 + round(3 * log(task.D))) * 10, 2, 0, 0.3 * task.bcRange() mu = int(round(lam / 2)) w = log(mu + 0.5) - log(range(1, mu + 1)) w = w / sum(w) mueff = 1 / sum(w ** 2) cs = (mueff + 2) / (task.D + mueff + 5) ds = 1 + cs + 2 * max(sqrt((mueff - 1) / (task.D + 1)) - 1, 0) ENN = sqrt(task.D) * (1 - 1 / (4 * task.D) + 1 / (21 * task.D ** 2)) cc, c1 = (4 + mueff / task.D) / (4 + task.D + 2 * mueff / task.D), 2 / ((task.D + 1.3) ** 2 + mueff) cmu, hth = min(1 - c1, alpha_mu * (mueff - 2 + 1 / mueff) / ((task.D + 2) ** 2 + alpha_mu * mueff / 2)), (1.4 + 2 / (task.D + 1)) * ENN ps, pc, C, sigma, M = full(task.D, 0.0), full(task.D, 0.0), eye(task.D), sigma0, full(task.D, 0.0) x = rnd.uniform(task.bcLower(), task.bcUpper()) x_f = task.eval(x) while not task.stopCondI(): pop_step = asarray([rnd.multivariate_normal(full(task.D, 0.0), C) for _ in range(int(lam))]) pop = asarray([task.repair(x + sigma * ps, rnd) for ps in pop_step]) pop_f = apply_along_axis(task.eval, 1, pop) isort = argsort(pop_f) pop, pop_f, pop_step = pop[isort[:mu]], pop_f[isort[:mu]], pop_step[isort[:mu]] if pop_f[0] < x_f: x, x_f = pop[0], pop_f[0] M = sum(w * pop_step.T, axis=1) ps = solve(chol(C).conj() + epsilon, ((1 - cs) * ps + sqrt(cs * (2 - cs) * mueff) * M + epsilon).T)[0].T sigma *= exp(cs / ds * (norm(ps) / ENN - 1)) ** 0.3 ifix = where(sigma == inf) if any(ifix): sigma[ifix] = sigma0 if norm(ps) / sqrt(1 - (1 - cs) ** (2 * (task.Iters + 1))) < hth: hs = 1 else: hs = 0 delta = (1 - hs) * cc * (2 - cc) pc = (1 - cc) * pc + hs * sqrt(cc * (2 - cc) * mueff) * M C = (1 - c1 - cmu) * C + c1 * (tile(pc, [len(pc), 1]) * tile(pc.reshape([len(pc), 1]), [1, len(pc)]) + delta * C) for i in range(mu): C += cmu * w[i] * tile(pop_step[i], [len(pop_step[i]), 1]) * tile(pop_step[i].reshape([len(pop_step[i]), 1]), [1, len(pop_step[i])]) E, V = eig(C) if any(E < epsilon): E = fmax(E, 0) C = lstsq(V.T, dot(V, diag(E)).T, rcond=None)[0].T return x, x_f
Example #28
Source File: test_module.py From awesome-semantic-segmentation-pytorch with Apache License 2.0 | 5 votes |
def _assert_tensor_close(a, b, atol=ATOL, rtol=EPS): npa, npb = a.cpu().numpy(), b.cpu().numpy() assert np.allclose(npa, npb, rtol=rtol, atol=atol), \ 'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format( a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max())
Example #29
Source File: sparsefuncs.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
def _sparse_nan_min_max(X, axis): return(_sparse_min_or_max(X, axis, np.fmin), _sparse_min_or_max(X, axis, np.fmax))
Example #30
Source File: test_umath.py From GraphicDesignPatternByPython with MIT License | 5 votes |
def test_complex_nans(self): nan = np.nan for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]: arg1 = np.array([0, cnan, cnan], dtype=complex) arg2 = np.array([cnan, 0, cnan], dtype=complex) out = np.array([0, 0, nan], dtype=complex) assert_equal(np.fmax(arg1, arg2), out)