Python numpy.isposinf() Examples
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Example #1
Source File: util.py From gluon-ts with Apache License 2.0 | 6 votes |
def jsonify_floats(json_object): """ Traverses through the JSON object and converts non JSON-spec compliant floats(nan, -inf, inf) to their string representations. Parameters ---------- json_object JSON object """ if isinstance(json_object, dict): return {k: jsonify_floats(v) for k, v in json_object.items()} elif isinstance(json_object, list): return [jsonify_floats(item) for item in json_object] elif isinstance(json_object, float): if np.isnan(json_object): return "NaN" elif np.isposinf(json_object): return "Infinity" elif np.isneginf(json_object): return "-Infinity" return json_object return json_object
Example #2
Source File: discretization.py From nevergrad with MIT License | 6 votes |
def probabilities(self) -> np.ndarray: """Creates the probability matrix from the weights """ axis = 1 maxv = np.max(self.weights, axis=1, keepdims=True) hasposinf = np.isposinf(maxv) maxv[np.isinf(maxv)] = 0 # avoid indeterminations exp: np.ndarray = np.exp(self.weights - maxv) # deal with infinite positives special case # by ignoring (0 proba) non-infinte on same row if np.any(hasposinf): is_inf = np.isposinf(self.weights) is_ignored = np.logical_and(np.logical_not(is_inf), hasposinf) exp[is_inf] = 1 exp[is_ignored] = 0 # random choice if sums to 0 sums0 = np.sum(exp, axis=axis) == 0 exp[sums0, :] = 1 exp /= np.sum(exp, axis=axis, keepdims=True) # normalize return exp
Example #3
Source File: test_node.py From onnx-tensorflow with Apache License 2.0 | 6 votes |
def test_is_inf(self): if legacy_opset_pre_ver(10): raise unittest.SkipTest("ONNX version {} doesn't support IsInf.".format( defs.onnx_opset_version())) input = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf], dtype=np.float32) expected_output = { "node_def": np.isinf(input), "node_def_neg_false": np.isposinf(input), "node_def_pos_false": np.isneginf(input) } node_defs = { "node_def": helper.make_node("IsInf", ["X"], ["Y"]), "node_def_neg_false": helper.make_node("IsInf", ["X"], ["Y"], detect_negative=0), "node_def_pos_false": helper.make_node("IsInf", ["X"], ["Y"], detect_positive=0) } for key in node_defs: output = run_node(node_defs[key], [input]) np.testing.assert_equal(output["Y"], expected_output[key])
Example #4
Source File: test_ufunclike.py From recruit with Apache License 2.0 | 6 votes |
def test_scalar(self): x = np.inf actual = np.isposinf(x) expected = np.True_ assert_equal(actual, expected) assert_equal(type(actual), type(expected)) x = -3.4 actual = np.fix(x) expected = np.float64(-3.0) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) out = np.array(0.0) actual = np.fix(x, out=out) assert_(actual is out)
Example #5
Source File: test_ufunclike.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
def test_scalar(self): x = np.inf actual = np.isposinf(x) expected = np.True_ assert_equal(actual, expected) assert_equal(type(actual), type(expected)) x = -3.4 actual = np.fix(x) expected = np.float64(-3.0) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) out = np.array(0.0) actual = np.fix(x, out=out) assert_(actual is out)
Example #6
Source File: test_ufunclike.py From vnpy_crypto with MIT License | 6 votes |
def test_scalar(self): x = np.inf actual = np.isposinf(x) expected = np.True_ assert_equal(actual, expected) assert_equal(type(actual), type(expected)) x = -3.4 actual = np.fix(x) expected = np.float64(-3.0) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) out = np.array(0.0) actual = np.fix(x, out=out) assert_(actual is out)
Example #7
Source File: converters.py From Carnets with BSD 3-Clause "New" or "Revised" License | 6 votes |
def output(self, value, mask): if mask: return self._null_output if np.isfinite(value): if not np.isscalar(value): value = value.dtype.type(value) result = self._output_format.format(value) if result.startswith('array'): raise RuntimeError() if (self._output_format[2] == 'r' and result.endswith('.0')): result = result[:-2] return result elif np.isnan(value): return 'NaN' elif np.isposinf(value): return '+InF' elif np.isneginf(value): return '-InF' # Should never raise vo_raise(f"Invalid floating point value '{value}'")
Example #8
Source File: test_ufunclike.py From twitter-stock-recommendation with MIT License | 6 votes |
def test_scalar(self): x = np.inf actual = np.isposinf(x) expected = np.True_ assert_equal(actual, expected) assert_equal(type(actual), type(expected)) x = -3.4 actual = np.fix(x) expected = np.float64(-3.0) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) out = np.array(0.0) actual = np.fix(x, out=out) assert_(actual is out)
Example #9
Source File: test_ufunclike.py From coffeegrindsize with MIT License | 6 votes |
def test_scalar(self): x = np.inf actual = np.isposinf(x) expected = np.True_ assert_equal(actual, expected) assert_equal(type(actual), type(expected)) x = -3.4 actual = np.fix(x) expected = np.float64(-3.0) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) out = np.array(0.0) actual = np.fix(x, out=out) assert_(actual is out)
Example #10
Source File: test_ufunclike.py From elasticintel with GNU General Public License v3.0 | 6 votes |
def test_scalar(self): x = np.inf actual = np.isposinf(x) expected = np.True_ assert_equal(actual, expected) assert_equal(type(actual), type(expected)) x = -3.4 actual = np.fix(x) expected = np.float64(-3.0) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) out = np.array(0.0) actual = np.fix(x, out=out) assert_(actual is out)
Example #11
Source File: test_ufunclike.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def test_scalar(self): x = np.inf actual = np.isposinf(x) expected = np.True_ assert_equal(actual, expected) assert_equal(type(actual), type(expected)) x = -3.4 actual = np.fix(x) expected = np.float64(-3.0) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) out = np.array(0.0) actual = np.fix(x, out=out) assert_(actual is out)
Example #12
Source File: test_ufunclike.py From GraphicDesignPatternByPython with MIT License | 6 votes |
def test_scalar(self): x = np.inf actual = np.isposinf(x) expected = np.True_ assert_equal(actual, expected) assert_equal(type(actual), type(expected)) x = -3.4 actual = np.fix(x) expected = np.float64(-3.0) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) out = np.array(0.0) actual = np.fix(x, out=out) assert_(actual is out)
Example #13
Source File: test_basic.py From GraphicDesignPatternByPython with MIT License | 6 votes |
def test_kl_div(): def xfunc(x, y): if x < 0 or y < 0 or (y == 0 and x != 0): # extension of natural domain to preserve convexity return np.inf elif np.isposinf(x) or np.isposinf(y): # limits within the natural domain return np.inf elif x == 0: return y else: return special.xlogy(x, x/y) - x + y values = (0, 0.5, 1.0) signs = [-1, 1] arr = [] for sgna, va, sgnb, vb in itertools.product(signs, values, signs, values): arr.append((sgna*va, sgnb*vb)) z = np.array(arr, dtype=float) w = np.vectorize(xfunc, otypes=[np.float64])(z[:,0], z[:,1]) assert_func_equal(special.kl_div, w, z, rtol=1e-13, atol=1e-13)
Example #14
Source File: numutils.py From cooltools with MIT License | 6 votes |
def fill_inf(arr, pos_value=0, neg_value=0, copy=True): """Replaces positive and negative infinity entries in an array with the provided values. Parameters ---------- arr : np.array pos_value : float Fill value for np.inf neg_value : float Fill value for -np.inf copy : bool, optional If True, creates a copy of x, otherwise replaces values in-place. By default, True. """ if copy: arr = arr.copy() arr[np.isposinf(arr)] = pos_value arr[np.isneginf(arr)] = neg_value return arr
Example #15
Source File: common.py From sparse with BSD 3-Clause "New" or "Revised" License | 6 votes |
def isposinf(x, out=None): """ Test element-wise for positive infinity, return result as sparse ``bool`` array. Parameters ---------- x Input out, optional Output array Examples -------- >>> import sparse >>> x = sparse.as_coo(np.array([np.inf])) >>> sparse.isposinf(x).todense() array([ True]) See Also -------- numpy.isposinf : The NumPy equivalent """ from .core import elemwise return elemwise(lambda x, out=None, dtype=None: np.isposinf(x, out=out), x, out=out)
Example #16
Source File: test_ufunclike.py From mxnet-lambda with Apache License 2.0 | 6 votes |
def test_scalar(self): x = np.inf actual = np.isposinf(x) expected = np.True_ assert_equal(actual, expected) assert_equal(type(actual), type(expected)) x = -3.4 actual = np.fix(x) expected = np.float64(-3.0) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) out = np.array(0.0) actual = np.fix(x, out=out) assert_(actual is out)
Example #17
Source File: test_ufunclike.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def test_scalar(self): x = np.inf actual = np.isposinf(x) expected = np.True_ assert_equal(actual, expected) assert_equal(type(actual), type(expected)) x = -3.4 actual = np.fix(x) expected = np.float64(-3.0) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) out = np.array(0.0) actual = np.fix(x, out=out) assert_(actual is out)
Example #18
Source File: test_ufunclike.py From pySINDy with MIT License | 6 votes |
def test_scalar(self): x = np.inf actual = np.isposinf(x) expected = np.True_ assert_equal(actual, expected) assert_equal(type(actual), type(expected)) x = -3.4 actual = np.fix(x) expected = np.float64(-3.0) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) out = np.array(0.0) actual = np.fix(x, out=out) assert_(actual is out)
Example #19
Source File: test_quantity_non_ufuncs.py From Carnets with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_isposinf(self): self.check(np.isposinf)
Example #20
Source File: test_ufunclike.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def test_isposinf(self): a = nx.array([nx.inf, -nx.inf, nx.nan, 0.0, 3.0, -3.0]) out = nx.zeros(a.shape, bool) tgt = nx.array([True, False, False, False, False, False]) res = ufl.isposinf(a) assert_equal(res, tgt) res = ufl.isposinf(a, out) assert_equal(res, tgt) assert_equal(out, tgt)
Example #21
Source File: test_ufunclike.py From coffeegrindsize with MIT License | 5 votes |
def test_isposinf(self): a = nx.array([nx.inf, -nx.inf, nx.nan, 0.0, 3.0, -3.0]) out = nx.zeros(a.shape, bool) tgt = nx.array([True, False, False, False, False, False]) res = ufl.isposinf(a) assert_equal(res, tgt) res = ufl.isposinf(a, out) assert_equal(res, tgt) assert_equal(out, tgt) a = a.astype(np.complex) with assert_raises(TypeError): ufl.isposinf(a)
Example #22
Source File: test_ufunclike.py From elasticintel with GNU General Public License v3.0 | 5 votes |
def test_deprecated(self): # NumPy 1.13.0, 2017-04-26 assert_warns(DeprecationWarning, ufl.fix, [1, 2], y=nx.empty(2)) assert_warns(DeprecationWarning, ufl.isposinf, [1, 2], y=nx.empty(2)) assert_warns(DeprecationWarning, ufl.isneginf, [1, 2], y=nx.empty(2))
Example #23
Source File: test_ufunclike.py From coffeegrindsize with MIT License | 5 votes |
def test_deprecated(self): # NumPy 1.13.0, 2017-04-26 assert_warns(DeprecationWarning, ufl.fix, [1, 2], y=nx.empty(2)) assert_warns(DeprecationWarning, ufl.isposinf, [1, 2], y=nx.empty(2)) assert_warns(DeprecationWarning, ufl.isneginf, [1, 2], y=nx.empty(2))
Example #24
Source File: test_ufunclike.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_isposinf(self): a = nx.array([nx.inf, -nx.inf, nx.nan, 0.0, 3.0, -3.0]) out = nx.zeros(a.shape, bool) tgt = nx.array([True, False, False, False, False, False]) res = ufl.isposinf(a) assert_equal(res, tgt) res = ufl.isposinf(a, out) assert_equal(res, tgt) assert_equal(out, tgt)
Example #25
Source File: test_ufunclike.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_deprecated(self): # NumPy 1.13.0, 2017-04-26 assert_warns(DeprecationWarning, ufl.fix, [1, 2], y=nx.empty(2)) assert_warns(DeprecationWarning, ufl.isposinf, [1, 2], y=nx.empty(2)) assert_warns(DeprecationWarning, ufl.isneginf, [1, 2], y=nx.empty(2))
Example #26
Source File: test_ufunclike.py From mxnet-lambda with Apache License 2.0 | 5 votes |
def test_deprecated(self): # NumPy 1.13.0, 2017-04-26 assert_warns(DeprecationWarning, ufl.fix, [1, 2], y=nx.empty(2)) assert_warns(DeprecationWarning, ufl.isposinf, [1, 2], y=nx.empty(2)) assert_warns(DeprecationWarning, ufl.isneginf, [1, 2], y=nx.empty(2))
Example #27
Source File: test_ufunclike.py From mxnet-lambda with Apache License 2.0 | 5 votes |
def test_isposinf(self): a = nx.array([nx.inf, -nx.inf, nx.nan, 0.0, 3.0, -3.0]) out = nx.zeros(a.shape, bool) tgt = nx.array([True, False, False, False, False, False]) res = ufl.isposinf(a) assert_equal(res, tgt) res = ufl.isposinf(a, out) assert_equal(res, tgt) assert_equal(out, tgt)
Example #28
Source File: test_erf.py From cupy with MIT License | 5 votes |
def test_erfcinv_behavior(self, dtype): a = cupy.empty((1,), dtype=dtype) a[:] = 2.0 + 1E-6 a = cupyx.scipy.special.erfcinv(a) assert cupy.isnan(a) a[:] = 0.0 - 1E-6 a = cupyx.scipy.special.erfcinv(a) assert cupy.isnan(a) a[:] = 0.0 a = cupyx.scipy.special.erfcinv(a) assert numpy.isposinf(cupy.asnumpy(a)) a[:] = 2.0 a = cupyx.scipy.special.erfcinv(a) assert numpy.isneginf(cupy.asnumpy(a))
Example #29
Source File: test_ufunclike.py From pySINDy with MIT License | 5 votes |
def test_deprecated(self): # NumPy 1.13.0, 2017-04-26 assert_warns(DeprecationWarning, ufl.fix, [1, 2], y=nx.empty(2)) assert_warns(DeprecationWarning, ufl.isposinf, [1, 2], y=nx.empty(2)) assert_warns(DeprecationWarning, ufl.isneginf, [1, 2], y=nx.empty(2))
Example #30
Source File: distributions.py From Neuraxle with Apache License 2.0 | 5 votes |
def _get_sum_starting_info(limits): if np.isinf(limits[0]) and np.isinf(limits[1]): raise ValueError("Cannot calculate a sum on infinite terms.") if np.isposinf(limits[0]): starting_value = limits[1] stop_value = limits[0] method = "increasing" elif np.isposinf(limits[1]): starting_value = limits[0] stop_value = limits[1] method = "increasing" elif np.isneginf(limits[0]): starting_value = limits[1] stop_value = limits[0] method = "decreasing" elif np.isneginf(limits[1]): starting_value = limits[0] stop_value = limits[1] method = "decreasing" elif np.greater(limits[1], limits[0]): starting_value = limits[0] stop_value = limits[1] method = "increasing" else: starting_value = limits[1] stop_value = limits[0] method = "increasing" return method, starting_value, stop_value