Python numpy.isinf() Examples
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Example #1
Source File: data_helper.py From LanczosNetwork with MIT License | 7 votes |
def normalize_adj(A, is_sym=True, exponent=0.5): """ Normalize adjacency matrix is_sym=True: D^{-1/2} A D^{-1/2} is_sym=False: D^{-1} A """ rowsum = np.array(A.sum(1)) if is_sym: r_inv = np.power(rowsum, -exponent).flatten() else: r_inv = np.power(rowsum, -1.0).flatten() r_inv[np.isinf(r_inv)] = 0. if sp.isspmatrix(A): r_mat_inv = sp.diags(r_inv.squeeze()) else: r_mat_inv = np.diag(r_inv) if is_sym: return r_mat_inv.dot(A).dot(r_mat_inv) else: return r_mat_inv.dot(A)
Example #2
Source File: test_numeric.py From recruit with Apache License 2.0 | 6 votes |
def test_double(self): # offset for alignment test for i in range(2): assert_array_equal(self.d[i:] > 0, self.ed[i:]) assert_array_equal(self.d[i:] - 1 >= 0, self.ed[i:]) assert_array_equal(self.d[i:] == 0, ~self.ed[i:]) assert_array_equal(-self.d[i:] < 0, self.ed[i:]) assert_array_equal(-self.d[i:] + 1 <= 0, self.ed[i:]) r = self.d[i:] != 0 assert_array_equal(r, self.ed[i:]) r2 = self.d[i:] != np.zeros_like(self.d[i:]) r3 = 0 != self.d[i:] assert_array_equal(r, r2) assert_array_equal(r, r3) # check bool == 0x1 assert_array_equal(r.view(np.int8), r.astype(np.int8)) assert_array_equal(r2.view(np.int8), r2.astype(np.int8)) assert_array_equal(r3.view(np.int8), r3.astype(np.int8)) # isnan on amd64 takes the same code path assert_array_equal(np.isnan(self.nd[i:]), self.ed[i:]) assert_array_equal(np.isfinite(self.nd[i:]), ~self.ed[i:]) assert_array_equal(np.isfinite(self.infd[i:]), ~self.ed[i:]) assert_array_equal(np.isinf(self.infd[i:]), self.ednonan[i:]) assert_array_equal(np.signbit(self.signd[i:]), self.ed[i:])
Example #3
Source File: gaussian_moments.py From DOTA_models with Apache License 2.0 | 6 votes |
def _compute_eps(log_moments, delta): """Compute epsilon for given log_moments and delta. Args: log_moments: the log moments of privacy loss, in the form of pairs of (moment_order, log_moment) delta: the target delta. Returns: epsilon """ min_eps = float("inf") for moment_order, log_moment in log_moments: if moment_order == 0: continue if math.isinf(log_moment) or math.isnan(log_moment): sys.stderr.write("The %d-th order is inf or Nan\n" % moment_order) continue min_eps = min(min_eps, (log_moment - math.log(delta)) / moment_order) return min_eps
Example #4
Source File: gaussian_moments.py From DOTA_models with Apache License 2.0 | 6 votes |
def _compute_delta(log_moments, eps): """Compute delta for given log_moments and eps. Args: log_moments: the log moments of privacy loss, in the form of pairs of (moment_order, log_moment) eps: the target epsilon. Returns: delta """ min_delta = 1.0 for moment_order, log_moment in log_moments: if moment_order == 0: continue if math.isinf(log_moment) or math.isnan(log_moment): sys.stderr.write("The %d-th order is inf or Nan\n" % moment_order) continue if log_moment < moment_order * eps: min_delta = min(min_delta, math.exp(log_moment - moment_order * eps)) return min_delta
Example #5
Source File: data_collection.py From dataiku-contrib with Apache License 2.0 | 6 votes |
def _get_numeric_feature_analysis_data(self, series, output): logger.info("Checking series of type: %s (isM8=%s)" % (series.dtype, series.dtype == np.dtype('M8[ns]'))) if np.isinf(series).any(): raise ValueError("Numeric feature '%s' contains Infinity values" % name) output['stats'] = { 'min': series.min(), 'average': series.mean(), 'median': series.median(), 'max': series.max(), 'p99': series.quantile(0.99), 'std': series.std() } output['nulls_count'] = series.isnull().sum() return output
Example #6
Source File: test_numeric.py From recruit with Apache License 2.0 | 6 votes |
def test_float(self): # offset for alignment test for i in range(4): assert_array_equal(self.f[i:] > 0, self.ef[i:]) assert_array_equal(self.f[i:] - 1 >= 0, self.ef[i:]) assert_array_equal(self.f[i:] == 0, ~self.ef[i:]) assert_array_equal(-self.f[i:] < 0, self.ef[i:]) assert_array_equal(-self.f[i:] + 1 <= 0, self.ef[i:]) r = self.f[i:] != 0 assert_array_equal(r, self.ef[i:]) r2 = self.f[i:] != np.zeros_like(self.f[i:]) r3 = 0 != self.f[i:] assert_array_equal(r, r2) assert_array_equal(r, r3) # check bool == 0x1 assert_array_equal(r.view(np.int8), r.astype(np.int8)) assert_array_equal(r2.view(np.int8), r2.astype(np.int8)) assert_array_equal(r3.view(np.int8), r3.astype(np.int8)) # isnan on amd64 takes the same code path assert_array_equal(np.isnan(self.nf[i:]), self.ef[i:]) assert_array_equal(np.isfinite(self.nf[i:]), ~self.ef[i:]) assert_array_equal(np.isfinite(self.inff[i:]), ~self.ef[i:]) assert_array_equal(np.isinf(self.inff[i:]), self.efnonan[i:]) assert_array_equal(np.signbit(self.signf[i:]), self.ef[i:])
Example #7
Source File: test_scalarmath.py From recruit with Apache License 2.0 | 6 votes |
def test_zero_division(self): with np.errstate(all="ignore"): for t in [np.complex64, np.complex128]: a = t(0.0) b = t(1.0) assert_(np.isinf(b/a)) b = t(complex(np.inf, np.inf)) assert_(np.isinf(b/a)) b = t(complex(np.inf, np.nan)) assert_(np.isinf(b/a)) b = t(complex(np.nan, np.inf)) assert_(np.isinf(b/a)) b = t(complex(np.nan, np.nan)) assert_(np.isnan(b/a)) b = t(0.) assert_(np.isnan(b/a))
Example #8
Source File: test_analytics.py From recruit with Apache License 2.0 | 6 votes |
def test_numpy_type_funcs(func): # for func in [np.isfinite, np.isinf, np.isnan, np.signbit]: # copy and paste from idx fixture as pytest doesn't support # parameters and fixtures at the same time. major_axis = Index(['foo', 'bar', 'baz', 'qux']) minor_axis = Index(['one', 'two']) major_codes = np.array([0, 0, 1, 2, 3, 3]) minor_codes = np.array([0, 1, 0, 1, 0, 1]) index_names = ['first', 'second'] idx = MultiIndex( levels=[major_axis, minor_axis], codes=[major_codes, minor_codes], names=index_names, verify_integrity=False ) with pytest.raises(Exception): func(idx)
Example #9
Source File: test_reductions.py From recruit with Apache License 2.0 | 6 votes |
def test_sum_inf(self): s = Series(np.random.randn(10)) s2 = s.copy() s[5:8] = np.inf s2[5:8] = np.nan assert np.isinf(s.sum()) arr = np.random.randn(100, 100).astype('f4') arr[:, 2] = np.inf with pd.option_context("mode.use_inf_as_na", True): tm.assert_almost_equal(s.sum(), s2.sum()) res = nanops.nansum(arr, axis=1) assert np.isinf(res).all()
Example #10
Source File: minibatch2.py From TFFRCNN with MIT License | 6 votes |
def _get_viewpoint_estimation_labels(viewpoint_data, clss, num_classes): """Bounding-box regression targets are stored in a compact form in the roidb. This function expands those targets into the 4-of-4*K representation used by the network (i.e. only one class has non-zero targets). The loss weights are similarly expanded. Returns: view_target_data (ndarray): N x 3K blob of regression targets view_loss_weights (ndarray): N x 3K blob of loss weights """ view_targets = np.zeros((clss.size, 3 * num_classes), dtype=np.float32) view_loss_weights = np.zeros(view_targets.shape, dtype=np.float32) inds = np.where( (clss > 0) & np.isfinite(viewpoint_data[:,0]) & np.isfinite(viewpoint_data[:,1]) & np.isfinite(viewpoint_data[:,2]) )[0] for ind in inds: cls = clss[ind] start = 3 * cls end = start + 3 view_targets[ind, start:end] = viewpoint_data[ind, :] view_loss_weights[ind, start:end] = [1., 1., 1.] assert not np.isinf(view_targets).any(), 'viewpoint undefined' return view_targets, view_loss_weights
Example #11
Source File: testing.py From dexplo with BSD 3-Clause "New" or "Revised" License | 6 votes |
def _check_1d_arrays(a: ndarray, b: ndarray, kind: str, tol: float = 10 ** -4) -> bool: if kind == 'O': if not va.is_equal_1d_object(a, b): raise AssertionError(f'The values of the columns are not equal') return True elif kind == 'f': with np.errstate(invalid='ignore'): criteria1 = np.abs(a - b) < tol criteria2 = np.isnan(a) & np.isnan(b) criteria3 = np.isinf(a) & np.isinf(b) return (criteria1 | criteria2 | criteria3).all() else: try: np.testing.assert_array_equal(a, b) except AssertionError: return False return True
Example #12
Source File: ColorMapWidget.py From tf-pose with Apache License 2.0 | 6 votes |
def map(self, data): data = data[self.fieldName] colors = np.empty((len(data), 4)) default = np.array(fn.colorTuple(self['Default'])) / 255. colors[:] = default for v in self.param('Values'): mask = data == v.maskValue c = np.array(fn.colorTuple(v.value())) / 255. colors[mask] = c #scaled = np.clip((data-self['Min']) / (self['Max']-self['Min']), 0, 1) #cmap = self.value() #colors = cmap.map(scaled, mode='float') #mask = np.isnan(data) | np.isinf(data) #nanColor = self['NaN'] #nanColor = (nanColor.red()/255., nanColor.green()/255., nanColor.blue()/255., nanColor.alpha()/255.) #colors[mask] = nanColor return colors
Example #13
Source File: test_arithmetic_execution.py From mars with Apache License 2.0 | 6 votes |
def testDtypeExecution(self): a = ones((10, 20), dtype='f4', chunk_size=5) c = truediv(a, 2, dtype='f8') res = self.executor.execute_tensor(c, concat=True)[0] self.assertEqual(res.dtype, np.float64) c = truediv(a, 0, dtype='f8') res = self.executor.execute_tensor(c, concat=True)[0] self.assertTrue(np.isinf(res[0, 0])) with self.assertRaises(FloatingPointError): with np.errstate(divide='raise'): c = truediv(a, 0, dtype='f8') _ = self.executor.execute_tensor(c, concat=True)[0] # noqa: F841
Example #14
Source File: Scalers.py From scattertext with Apache License 2.0 | 6 votes |
def scale_neg_1_to_1_with_zero_mean_log_abs_max(v): ''' !!! not working ''' df = pd.DataFrame({'v':v, 'sign': (v > 0) * 2 - 1}) df['lg'] = np.log(np.abs(v)) / np.log(1.96) df['exclude'] = (np.isinf(df.lg) | np.isneginf(df.lg)) for mask in [(df['sign'] == -1) & (df['exclude'] == False), (df['sign'] == 1) & (df['exclude'] == False)]: df[mask]['lg'] = df[mask]['lg'].max() - df[mask]['lg'] df['lg'] *= df['sign'] df['lg'] = df['lg'].fillna(0) print(df[df['exclude']]['lg'].values) #to_rescale = convention_df['lg'].reindex(v.index) df['to_out'] = scale_neg_1_to_1_with_zero_mean_abs_max(df['lg']) print('right') print(df.sort_values(by='lg').iloc[:5]) print(df.sort_values(by='lg').iloc[-5:]) print('to_out') print(df.sort_values(by='to_out').iloc[:5]) print(df.sort_values(by='to_out').iloc[-5:]) print(len(df), len(df.dropna())) return df['to_out']
Example #15
Source File: core.py From ffn with MIT License | 6 votes |
def calc_inv_vol_weights(returns): """ Calculates weights proportional to inverse volatility of each column. Returns weights that are inversely proportional to the column's volatility resulting in a set of portfolio weights where each position has the same level of volatility. Note, that assets with returns all equal to NaN or 0 are excluded from the portfolio (their weight is set to NaN). Returns: Series {col_name: weight} """ # calc vols vol = np.divide(1., np.std(returns, ddof=1)) vol[np.isinf(vol)] = np.NaN volsum = vol.sum() return np.divide(vol, volsum)
Example #16
Source File: _distn_infrastructure.py From lambda-packs with MIT License | 6 votes |
def _entropy(self, *args): def integ(x): val = self._pdf(x, *args) return entr(val) # upper limit is often inf, so suppress warnings when integrating olderr = np.seterr(over='ignore') h = integrate.quad(integ, self.a, self.b)[0] np.seterr(**olderr) if not np.isnan(h): return h else: # try with different limits if integration problems low, upp = self.ppf([1e-10, 1. - 1e-10], *args) if np.isinf(self.b): upper = upp else: upper = self.b if np.isinf(self.a): lower = low else: lower = self.a return integrate.quad(integ, lower, upper)[0]
Example #17
Source File: utilities.py From qcqp with MIT License | 5 votes |
def eval(self, x): if np.isinf(x): if self.P != 0: return self.P*x*x if self.q != 0: return self.q*x return r return x*(self.P*x + self.q) + self.r
Example #18
Source File: math.py From formulas with European Union Public License 1.1 | 5 votes |
def xmround(*args): raise_errors(args) num, sig = list(flatten(map(replace_empty, args), None)) if isinstance(num, bool) or isinstance(sig, bool): return Error.errors['#VALUE!'] with np.errstate(divide='ignore', invalid='ignore'): x = num < 0 < sig and np.nan or xceiling(num, sig, ceil=np.round) return (np.isnan(x) or np.isinf(x)) and Error.errors['#NUM!'] or x
Example #19
Source File: nested_choice_calcs.py From pylogit with BSD 3-Clause "New" or "Revised" License | 5 votes |
def naturalize_nest_coefs(nest_coef_estimates): """ Parameters ---------- nest_coef_estimates : 1D ndarray. Should contain the estimated logit's (`ln[nest_coefs / (1 - nest_coefs)]`) of the true nest coefficients. All values should be ints, floats, or longs. Returns ------- nest_coefs : 1D ndarray. Will contain the 'natural' nest coefficients: `1.0 / (1.0 + exp(-nest_coef_estimates))`. """ # Calculate the exponential term of the # logistic transformation exp_term = np.exp(-1 * nest_coef_estimates) # Guard against_overflow inf_idx = np.isinf(exp_term) exp_term[inf_idx] = max_comp_value # Calculate the 'natural' nest coefficients nest_coefs = 1.0 / (1.0 + exp_term) # Guard against underflow zero_idx = (nest_coefs == 0) nest_coefs[zero_idx] = min_comp_value return nest_coefs # Create the actual function used to calculate the gradient
Example #20
Source File: test_piecewise.py From pywr with GNU General Public License v3.0 | 5 votes |
def test_piecewise_with_parameters_json(): """Test using parameters with piecewise link.""" model = load_model("piecewise1_with_parameters.json") sublinks = model.nodes["link1"].sublinks assert isinstance(sublinks[0].max_flow, ConstantParameter) assert np.isinf(sublinks[1].max_flow) assert isinstance(sublinks[0].cost, ConstantParameter) assert isinstance(sublinks[1].cost, ConstantParameter) model.run() assert_allclose(model.nodes["demand1"].flow, 20)
Example #21
Source File: utils.py From graph-cnn.pytorch with MIT License | 5 votes |
def normalize_adj(mx): """Row-normalize sparse matrix""" rowsum = np.array(mx.sum(1)) r_inv_sqrt = np.power(rowsum, -0.5).flatten() r_inv_sqrt[np.isinf(r_inv_sqrt)] = 0. r_mat_inv_sqrt = sp.diags(r_inv_sqrt) return mx.dot(r_mat_inv_sqrt).transpose().dot(r_mat_inv_sqrt).tocoo()
Example #22
Source File: base_multinomial_cm_v2.py From pylogit with BSD 3-Clause "New" or "Revised" License | 5 votes |
def ensure_valid_nums_in_specification_cols(specification, dataframe): """ Checks whether each column in `specification` contains numeric data, excluding positive or negative infinity and excluding NaN. Raises ValueError if any of the columns do not meet these requirements. Parameters ---------- specification : iterable of column headers in `dataframe`. dataframe : pandas DataFrame. Dataframe containing the data for the choice model to be estimated. Returns ------- None. """ problem_cols = [] for col in specification: # The condition below checks for values that are not floats or integers # This will catch values that are strings. if dataframe[col].dtype.kind not in ['f', 'i', 'u']: problem_cols.append(col) # The condition below checks for positive or negative inifinity values. elif np.isinf(dataframe[col]).any(): problem_cols.append(col) # This condition will check for NaN values. elif np.isnan(dataframe[col]).any(): problem_cols.append(col) if problem_cols != []: msg = "The following columns contain either +/- inifinity values, " msg_2 = "NaN values, or values that are not real numbers " msg_3 = "(e.g. strings):\n{}" total_msg = msg + msg_2 + msg_3 raise ValueError(total_msg.format(problem_cols)) return None
Example #23
Source File: math.py From formulas with European Union Public License 1.1 | 5 votes |
def xsrqtpi(number): raise_errors(number) x = list(flatten(replace_empty(number), None))[0] if isinstance(x, bool): return Error.errors['#VALUE!'] with np.errstate(divide='ignore', invalid='ignore'): x = np.sqrt(float(x) * np.pi) return (np.isnan(x) or np.isinf(x)) and Error.errors['#NUM!'] or x
Example #24
Source File: diffussion.py From manifold-diffusion with MIT License | 5 votes |
def normalize_connection_graph(G): W = csr_matrix(G) W = W - diags(W.diagonal()) D = np.array(1./ np.sqrt(W.sum(axis = 1))) D[np.isnan(D)] = 0 D[np.isinf(D)] = 0 D_mh = diags(D.reshape(-1)) Wn = D_mh * W * D_mh return Wn
Example #25
Source File: callbacks.py From astroNN with MIT License | 5 votes |
def on_batch_end(self, batch, logs=None): logs = logs or {} loss = logs.get('loss') if loss is not None: if np.isnan(loss) or np.isinf(loss): self.model.stop_training = True raise ValueError(f'Batch {int(batch)}: Invalid loss, terminating training')
Example #26
Source File: symbols.py From trees with Apache License 2.0 | 5 votes |
def estimate_norm(datas): if datas.shape[0] < 2: return None, None, 0.0 mp = np.mean(datas, axis=0) sp = np.cov(datas.transpose()) sign, logdet = np.linalg.slogdet(sp) if np.isnan(logdet) or np.isinf(logdet): return mp, sp, 0.0 ent = sign * logdet return mp, sp, ent
Example #27
Source File: entropy.py From Emotion-Recogniton-from-EEG-Signals with MIT License | 5 votes |
def corr(data,type_corr): C = np.array(data.corr(type_corr)) C[np.isnan(C)] = 0 C[np.isinf(C)] = 0 w,v = np.linalg.eig(C) #print(w) x = np.sort(w) x = np.real(x) return x
Example #28
Source File: functions.py From tf-pose with Apache License 2.0 | 5 votes |
def siScale(x, minVal=1e-25, allowUnicode=True): """ Return the recommended scale factor and SI prefix string for x. Example:: siScale(0.0001) # returns (1e6, 'μ') # This indicates that the number 0.0001 is best represented as 0.0001 * 1e6 = 100 μUnits """ if isinstance(x, decimal.Decimal): x = float(x) try: if np.isnan(x) or np.isinf(x): return(1, '') except: print(x, type(x)) raise if abs(x) < minVal: m = 0 x = 0 else: m = int(np.clip(np.floor(np.log(abs(x))/np.log(1000)), -9.0, 9.0)) if m == 0: pref = '' elif m < -8 or m > 8: pref = 'e%d' % (m*3) else: if allowUnicode: pref = SI_PREFIXES[m+8] else: pref = SI_PREFIXES_ASCII[m+8] p = .001**m return (p, pref)
Example #29
Source File: AxisItem.py From tf-pose with Apache License 2.0 | 5 votes |
def setRange(self, mn, mx): """Set the range of values displayed by the axis. Usually this is handled automatically by linking the axis to a ViewBox with :func:`linkToView <pyqtgraph.AxisItem.linkToView>`""" if any(np.isinf((mn, mx))) or any(np.isnan((mn, mx))): raise Exception("Not setting range to [%s, %s]" % (str(mn), str(mx))) self.range = [mn, mx] if self.autoSIPrefix: self.updateAutoSIPrefix() self.picture = None self.update()
Example #30
Source File: ColorMapWidget.py From tf-pose with Apache License 2.0 | 5 votes |
def map(self, data): data = data[self.fieldName] scaled = np.clip((data-self['Min']) / (self['Max']-self['Min']), 0, 1) cmap = self.value() colors = cmap.map(scaled, mode='float') mask = np.isnan(data) | np.isinf(data) nanColor = self['NaN'] nanColor = (nanColor.red()/255., nanColor.green()/255., nanColor.blue()/255., nanColor.alpha()/255.) colors[mask] = nanColor return colors