Python numpy.NINF Examples
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Example #1
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 6 votes |
def test_df_rolling_corr(self): all_data = [ list(range(10)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] length = min(len(d) for d in all_data) data = {n: d[:length] for n, d in zip(string.ascii_uppercase, all_data)} df = pd.DataFrame(data) for d in all_data: other = pd.Series(d) self._test_rolling_corr(df, other) other_all_data = deepcopy(all_data) + [list(range(10))[::-1]] other_all_data[1] = [-1., 1., 0., -0.1, 0.1, 0.] other_length = min(len(d) for d in other_all_data) other_data = {n: d[:other_length] for n, d in zip(string.ascii_uppercase, other_all_data)} other = pd.DataFrame(other_data) self._test_rolling_corr(df, other)
Example #2
Source File: util.py From revscoring with MIT License | 6 votes |
def normalize(v): if isinstance(v, numpy.bool_): return bool(v) elif isinstance(v, numpy.ndarray): return [normalize(item) for item in v] elif v == numpy.NaN: return "NaN" elif v == numpy.NINF: return "-Infinity" elif v == numpy.PINF: return "Infinity" elif isinstance(v, numpy.float): return float(v) elif isinstance(v, tuple): return list(v) else: return v
Example #3
Source File: Model.py From stocknet-code with MIT License | 6 votes |
def _create_corpus_embed(self): """ msg_embed: batch_size * max_n_days * max_n_msgs * msg_embed_size => corpus_embed: batch_size * max_n_days * corpus_embed_size """ with tf.name_scope('corpus_embed'): with tf.variable_scope('u_t'): proj_u = self._linear(self.msg_embed, self.msg_embed_size, 'tanh', use_bias=False) w_u = tf.get_variable('w_u', shape=(self.msg_embed_size, 1), initializer=self.initializer) u = tf.reduce_mean(tf.tensordot(proj_u, w_u, axes=1), axis=-1) # batch_size * max_n_days * max_n_msgs mask_msgs = tf.sequence_mask(self.n_msgs_ph, maxlen=self.max_n_msgs, dtype=tf.bool, name='mask_msgs') ninf = tf.fill(tf.shape(mask_msgs), np.NINF) masked_score = tf.where(mask_msgs, u, ninf) u = neural.softmax(masked_score) # batch_size * max_n_days * max_n_msgs u = tf.where(tf.is_nan(u), tf.zeros_like(u), u) # replace nan with 0.0 u = tf.expand_dims(u, axis=-2) # batch_size * max_n_days * 1 * max_n_msgs corpus_embed = tf.matmul(u, self.msg_embed) # batch_size * max_n_days * 1 * msg_embed_size corpus_embed = tf.reduce_mean(corpus_embed, axis=-2) # batch_size * max_n_days * msg_embed_size self.corpus_embed = tf.nn.dropout(corpus_embed, keep_prob=1-self.dropout_ce, name='corpus_embed')
Example #4
Source File: test_constants.py From chainer with MIT License | 6 votes |
def test_constants(): assert chainerx.Inf is numpy.Inf assert chainerx.Infinity is numpy.Infinity assert chainerx.NAN is numpy.NAN assert chainerx.NINF is numpy.NINF assert chainerx.NZERO is numpy.NZERO assert chainerx.NaN is numpy.NaN assert chainerx.PINF is numpy.PINF assert chainerx.PZERO is numpy.PZERO assert chainerx.e is numpy.e assert chainerx.euler_gamma is numpy.euler_gamma assert chainerx.inf is numpy.inf assert chainerx.infty is numpy.infty assert chainerx.nan is numpy.nan assert chainerx.newaxis is numpy.newaxis assert chainerx.pi is numpy.pi
Example #5
Source File: util.py From prpy with BSD 3-Clause "New" or "Revised" License | 6 votes |
def ComputeEnabledAABB(kinbody): """ Returns the AABB of the enabled links of a KinBody. @param kinbody: an OpenRAVE KinBody @returns: AABB of the enabled links of the KinBody """ from numpy import NINF, PINF from openravepy import AABB min_corner = numpy.array([PINF] * 3) max_corner = numpy.array([NINF] * 3) for link in kinbody.GetLinks(): if link.IsEnabled(): link_aabb = link.ComputeAABB() center = link_aabb.pos() half_extents = link_aabb.extents() min_corner = numpy.minimum(center - half_extents, min_corner) max_corner = numpy.maximum(center + half_extents, max_corner) center = (min_corner + max_corner) / 2. half_extents = (max_corner - min_corner) / 2. return AABB(center, half_extents)
Example #6
Source File: multi_label.py From ALiPy with BSD 3-Clause "New" or "Revised" License | 6 votes |
def lr_predict(self, BV, data, num_sub): BV = np.asarray(BV) data = np.asarray(data) fs = data.dot(BV) n = data.shape[0] n_class = int(fs.shape[1] / num_sub) pres = np.ones((n, n_class)) * np.NINF for j in range(num_sub): f = fs[:, j: fs.shape[1]: num_sub] assert (np.all(f.shape == pres.shape)) pres = np.fmax(pres, f) labels = -np.ones((n, n_class - 1)) for line in range(n_class - 1): gt = np.nonzero(pres[:, line] > pres[:, n_class - 1])[0] labels[gt, line] = 1 return pres, labels
Example #7
Source File: engine.py From hiscore with BSD 3-Clause "New" or "Revised" License | 6 votes |
def value_bounds(self, point): """ Returns the (lower_bound, upper_bound) tuple of a point implied by the reference set and the monotone relationship vector. Use it to improve and understand the reference set without triggering a MonotoneError. Returns np.inf as the second argument if there is no upper bound and np.NINF as the first argument if there is no lower bound. Required argument: point -- Point at which to assess upper and lower bounds. """ padj = point/self.scale points_greater_than = filter(lambda x: np.allclose(x,padj) or self.__monotone_rel__(x,padj)==1, self.points.keys()) points_less_than = filter(lambda x: np.allclose(x,padj) or self.__monotone_rel__(padj,x)==1, self.points.keys()) gtbound = np.inf if self.maxval is None else self.maxval ltbound = np.NINF if self.minval is None else self.minval for p in points_greater_than: gtbound = min(self.points[p],gtbound) for p in points_less_than: ltbound = max(self.points[p],ltbound) return ltbound, gtbound
Example #8
Source File: continuous_fidelity_entropy_search.py From emukit with Apache License 2.0 | 6 votes |
def _get_proposal_function(self, model, space): # Define proposal function for multi-fidelity ei = ExpectedImprovement(model) def proposal_func(x): x_ = x[None, :] # Map to highest fidelity idx = np.ones((x_.shape[0], 1)) * self.high_fidelity x_ = np.insert(x_, self.target_fidelity_index, idx, axis=1) if space.check_points_in_domain(x_): val = np.log(np.clip(ei.evaluate(x_)[0], 0., np.PINF)) if np.any(np.isnan(val)): return np.array([np.NINF]) else: return val else: return np.array([np.NINF]) return proposal_func
Example #9
Source File: entropy_search.py From emukit with Apache License 2.0 | 6 votes |
def _get_proposal_function(self, model, space): # Define proposal function for multi-fidelity ei = ExpectedImprovement(model) def proposal_func(x): x_ = x[None, :] # Add information source parameter into array idx = np.ones((x_.shape[0], 1)) * self.target_information_source_index x_ = np.insert(x_, self.source_idx, idx, axis=1) if space.check_points_in_domain(x_): val = np.log(np.clip(ei.evaluate(x_)[0], 0., np.PINF)) if np.any(np.isnan(val)): return np.array([np.NINF]) else: return val else: return np.array([np.NINF]) return proposal_func
Example #10
Source File: test_dynamic_shape.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())) inp = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf], dtype=np.float32) expected_output = np.isinf(inp) node_def = helper.make_node("IsInf", ["X"], ["Y"]) graph_def = helper.make_graph( [node_def], name="test_unknown_shape", inputs=[ helper.make_tensor_value_info("X", TensorProto.FLOAT, [None]), ], outputs=[helper.make_tensor_value_info("Y", TensorProto.BOOL, [None])]) tf_rep = onnx_graph_to_tensorflow_rep(graph_def) output = tf_rep.run({"X": inp}) np.testing.assert_equal(output["Y"], expected_output)
Example #11
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 #12
Source File: pytorch.py From incubator-tvm with Apache License 2.0 | 6 votes |
def _norm(): def _impl(inputs, input_types): data = inputs[0] dtype = input_types[0] axis = None keepdims = False if len(inputs) > 3: axis = list(_infer_shape(inputs[2])) keepdims = bool(inputs[3]) order = inputs[1] if order == np.inf: return _op.reduce.max(_op.abs(data), axis=axis, keepdims=keepdims) elif order == np.NINF: return _op.reduce.min(_op.abs(data), axis=axis, keepdims=keepdims) else: reci_order = _expr.const(1.0 / order, dtype=dtype) order = _expr.const(order) return _op.power(_op.reduce.sum(_op.power(_op.abs(data), order), axis=axis, keepdims=keepdims), reci_order) return _impl
Example #13
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 6 votes |
def test_df_rolling_cov(self): all_data = [ list(range(10)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] length = min(len(d) for d in all_data) data = {n: d[:length] for n, d in zip(string.ascii_uppercase, all_data)} df = pd.DataFrame(data) for d in all_data: other = pd.Series(d) self._test_rolling_cov(df, other) other_all_data = deepcopy(all_data) + [list(range(10))[::-1]] other_all_data[1] = [-1., 1., 0., -0.1, 0.1] other_length = min(len(d) for d in other_all_data) other_data = {n: d[:other_length] for n, d in zip(string.ascii_uppercase, other_all_data)} other = pd.DataFrame(other_data) self._test_rolling_cov(df, other)
Example #14
Source File: utils.py From PyVideoResearch with GNU General Public License v3.0 | 5 votes |
def charades_map(submission_array, gt_array): """ Approximate version of the charades evaluation function For precise numbers, use the submission file with the official matlab script """ fix = submission_array.copy() empty = np.sum(gt_array, axis=1) == 0 fix[empty, :] = np.NINF return map(fix, gt_array)
Example #15
Source File: cubefile.py From planetaryimage with BSD 3-Clause "New" or "Revised" License | 5 votes |
def apply_numpy_specials(self, copy=True): """Convert isis special pixel values to numpy special pixel values. ======= ======= Isis Numpy ======= ======= Null nan Lrs -inf Lis -inf His inf Hrs inf ======= ======= Parameters ---------- copy : bool [True] Whether to apply the new special values to a copy of the pixel data and leave the original unaffected Returns ------- Numpy Array A numpy array with special values converted to numpy's nan, inf, and -inf """ if copy: data = self.data.astype(numpy.float64) elif self.data.dtype != numpy.float64: data = self.data = self.data.astype(numpy.float64) else: data = self.data data[data == self.specials['Null']] = numpy.nan data[data < self.specials['Min']] = numpy.NINF data[data > self.specials['Max']] = numpy.inf return data
Example #16
Source File: correlated_likelihood.py From kombine with MIT License | 5 votes |
def log_prior(self, p): p = self.to_params(p) # Bounds if p['K'] < 0.0 or p['e'] < 0.0 or p['e'] > 1.0 or p['omega'] < 0.0 or p['omega'] > 2.0*np.pi or p['P'] < 0.0 or p['nu'] < 0.1 or p['nu'] > 10.0 or p['sigma'] < 0.0 or p['tau'] < 0.0 or p['tau'] > self.T: return np.NINF # Otherwise, flat prior on everything. return 0.0
Example #17
Source File: correlated_likelihood.py From kombine with MIT License | 5 votes |
def __call__(self, p): lp = self.log_prior(p) if lp == np.NINF: return np.NINF else: return lp + self.log_likelihood(p)
Example #18
Source File: test_umath.py From coffeegrindsize with MIT License | 5 votes |
def test_any_ninf(self): # atan2(+-y, -infinity) returns +-pi for finite y > 0. assert_almost_equal(ncu.arctan2(1, np.NINF), np.pi) assert_almost_equal(ncu.arctan2(-1, np.NINF), -np.pi)
Example #19
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
def test_series_rolling_cov_no_other(self): all_data = [ list(range(5)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] for data in all_data: series = pd.Series(data) self._test_rolling_cov_with_no_other(series)
Example #20
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
def test_series_rolling_cov(self): all_data = [ list(range(5)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] for main_data, other_data in product(all_data, all_data): series = pd.Series(main_data) other = pd.Series(other_data) self._test_rolling_cov(series, other)
Example #21
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
def test_series_rolling_corr_with_no_other(self): all_data = [ list(range(10)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] for data in all_data: series = pd.Series(data) self._test_rolling_corr_with_no_other(series)
Example #22
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
def test_series_rolling_var(self): all_data = [ list(range(10)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] indices = [list(range(len(data)))[::-1] for data in all_data] for data, index in zip(all_data, indices): series = pd.Series(data, index, name='A') self._test_rolling_var(series)
Example #23
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
def test_df_rolling_corr_no_other(self): all_data = [ list(range(10)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] length = min(len(d) for d in all_data) data = {n: d[:length] for n, d in zip(string.ascii_uppercase, all_data)} df = pd.DataFrame(data) self._test_rolling_corr_with_no_other(df)
Example #24
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
def test_series_rolling_corr(self): all_data = [ list(range(10)), [1., -1., 0., 0.1, -0.1], [-1., 1., 0., -0.1, 0.1, 0.], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] for main_data, other_data in product(all_data, all_data): series = pd.Series(main_data) other = pd.Series(other_data) self._test_rolling_corr(series, other)
Example #25
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
def test_series_rolling_apply_args(self): all_data = [ list(range(10)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] indices = [list(range(len(data)))[::-1] for data in all_data] for data, index in zip(all_data, indices): series = pd.Series(data, index, name='A') self._test_rolling_apply_args(series)
Example #26
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
def test_df_rolling_mean(self): all_data = [ list(range(10)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] length = min(len(d) for d in all_data) data = {n: d[:length] for n, d in zip(string.ascii_uppercase, all_data)} df = pd.DataFrame(data) self._test_rolling_mean(df)
Example #27
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
def test_df_rolling_quantile(self): all_data = [ list(range(10)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] length = min(len(d) for d in all_data) data = {n: d[:length] for n, d in zip(string.ascii_uppercase, all_data)} df = pd.DataFrame(data) self._test_rolling_quantile(df)
Example #28
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
def test_df_rolling_std(self): all_data = [ list(range(10)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] length = min(len(d) for d in all_data) data = {n: d[:length] for n, d in zip(string.ascii_uppercase, all_data)} df = pd.DataFrame(data) self._test_rolling_std(df)
Example #29
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
def test_df_rolling_var(self): all_data = [ list(range(10)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] length = min(len(d) for d in all_data) data = {n: d[:length] for n, d in zip(string.ascii_uppercase, all_data)} df = pd.DataFrame(data) self._test_rolling_var(df)
Example #30
Source File: test_rolling.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
def test_df_rolling_sum(self): all_data = [ list(range(10)), [1., -1., 0., 0.1, -0.1], [1., np.inf, np.inf, -1., 0., np.inf, np.NINF, np.NINF], [np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO] ] length = min(len(d) for d in all_data) data = {n: d[:length] for n, d in zip(string.ascii_uppercase, all_data)} df = pd.DataFrame(data) self._test_rolling_sum(df)