numpy.errstate

Here are the examples of the python api numpy.errstate taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.

152 Examples 7

Example 1

Project: scikit-learn
Source File: test_forest.py
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def check_probability(name):
    # Predict probabilities.
    ForestClassifier = FOREST_CLASSIFIERS[name]
    with np.errstate(divide="ignore"):
        clf = ForestClassifier(n_estimators=10, random_state=1, max_features=1,
                               max_depth=1)
        clf.fit(iris.data, iris.target)
        assert_array_almost_equal(np.sum(clf.predict_proba(iris.data), axis=1),
                                  np.ones(iris.data.shape[0]))
        assert_array_almost_equal(clf.predict_proba(iris.data),
                                  np.exp(clf.predict_log_proba(iris.data)))

Example 2

Project: scikit-learn
Source File: test_neighbors.py
View license
def _weight_func(dist):
    """ Weight function to replace lambda d: d ** -2.
    The lambda function is not valid because:
    if d==0 then 0^-2 is not valid. """

    # Dist could be multidimensional, flatten it so all values
    # can be looped
    with np.errstate(divide='ignore'):
        retval = 1. / dist
    return retval ** 2

Example 3

Project: scipy
Source File: linesearch.py
View license
def _quadmin(a, fa, fpa, b, fb):
    """
    Finds the minimizer for a quadratic polynomial that goes through
    the points (a,fa), (b,fb) with derivative at a of fpa,

    """
    # f(x) = B*(x-a)^2 + C*(x-a) + D
    with np.errstate(divide='raise', over='raise', invalid='raise'):
        try:
            D = fa
            C = fpa
            db = b - a * 1.0
            B = (fb - D - C * db) / (db * db)
            xmin = a - C / (2.0 * B)
        except ArithmeticError:
            return None
    if not np.isfinite(xmin):
        return None
    return xmin

Example 4

Project: scipy
Source File: test_slsqp.py
View license
    def test_minimize_bounded_approximated(self):
        # Minimize, method='SLSQP': bounded, approximated jacobian.
        with np.errstate(invalid='ignore'):
            res = minimize(self.fun, [-1.0, 1.0], args=(-1.0, ),
                           bounds=((2.5, None), (None, 0.5)),
                           method='SLSQP', options=self.opts)
        assert_(res['success'], res['message'])
        assert_allclose(res.x, [2.5, 0.5])
        assert_(2.5 <= res.x[0])
        assert_(res.x[1] <= 0.5)

Example 5

Project: sfs-python
Source File: util.py
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def db(x, power=False):
    """Convert *x* to decibel.

    Parameters
    ----------
    x : array_like
        Input data.  Values of 0 lead to negative infinity.
    power : bool, optional
        If ``power=False`` (the default), *x* is squared before
        conversion.

    """
    with np.errstate(divide='ignore'):
        return 10 if power else 20 * np.log10(np.abs(x))

Example 6

Project: bayespy
Source File: test_bernoulli.py
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    def test_random(self):
        """
        Test random sampling in Bernoulli node
        """
        p = [1.0, 0.0]
        with np.errstate(divide='ignore'):
            Z = Bernoulli(p, plates=(3,2)).random()
        self.assertArrayEqual(Z, np.ones((3,2))*p)

Example 7

Project: bayespy
Source File: test_binomial.py
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    def test_random(self):
        """
        Test random sampling in Binomial node
        """
        N = [ [5], [50] ]
        p = [1.0, 0.0]
        with np.errstate(divide='ignore'):
            Z = Binomial(N, p, plates=(3,2,2)).random()
        self.assertArrayEqual(Z, np.ones((3,2,2))*N*p)

Example 8

Project: paramz
Source File: parameterized_tests.py
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    def test_optimize_org_bfgs(self):
        import warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            with np.errstate(divide='ignore'):
                self.testmodel.optimize_restarts(1, messages=0, optimizer='org-bfgs', xtol=0, ftol=0, gtol=1e-6)
                self.testmodel.optimize(messages=1, optimizer='org-bfgs')
        np.testing.assert_array_less(self.testmodel.gradient, np.ones(self.testmodel.size)*1e-2)

Example 9

Project: gplearn
Source File: fitness.py
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def weighted_pearson(x1, x2, w):
    """Calculate the weighted Pearson correlation coefficient."""
    with np.errstate(divide='ignore', invalid='ignore'):
        x1_demean = x1 - np.average(x1, weights=w)
        x2_demean = x2 - np.average(x2, weights=w)
        corr = ((np.sum(w * x1_demean * x2_demean) / np.sum(w)) /
                np.sqrt((np.sum(w * x1_demean ** 2) *
                         np.sum(w * x2_demean ** 2)) /
                        (np.sum(w) ** 2)))
    if np.isfinite(corr):
        return np.abs(corr)
    return 0

Example 10

Project: datashader
Source File: reductions.py
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    @staticmethod
    def _finalize(bases, **kwargs):
        sums, counts = bases
        with np.errstate(divide='ignore', invalid='ignore'):
            x = sums/counts
        return xr.DataArray(x, **kwargs)

Example 11

Project: datashader
Source File: reductions.py
View license
    @staticmethod
    def _finalize(bases, **kwargs):
        sums, counts, m2s = bases
        with np.errstate(divide='ignore', invalid='ignore'):
            x = m2s/counts
        return xr.DataArray(x, **kwargs)

Example 12

Project: datashader
Source File: reductions.py
View license
    @staticmethod
    def _finalize(bases, **kwargs):
        sums, counts, m2s = bases
        with np.errstate(divide='ignore', invalid='ignore'):
            x = np.sqrt(m2s/counts)
        return xr.DataArray(x, **kwargs)

Example 13

Project: deap
Source File: symbreg_numpy.py
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def protectedDiv(left, right):
    with numpy.errstate(divide='ignore',invalid='ignore'):
        x = numpy.divide(left, right)
        if isinstance(x, numpy.ndarray):
            x[numpy.isinf(x)] = 1
            x[numpy.isnan(x)] = 1
        elif numpy.isinf(x) or numpy.isnan(x):
            x = 1
    return x

Example 14

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def sigmoid(z):
    """Computes sigmoid function.

    z: array of input values.

    Returns array of outputs, sigmoid(z).
    """
    # Note: this version of sigmoid tries to avoid overflows in the computation
    # of e^(-z), by using an alternative formulation when z is negative, to get
    # 0. e^z / (1+e^z) is equivalent to the definition of sigmoid, but we won't
    # get e^(-z) to overflow when z is very negative.
    # Since both the x and y arguments to np.where are evaluated by Python, we
    # may still get overflow warnings for large z elements; therefore we ignore
    # warnings during this computation.
    with np.errstate(over='ignore', invalid='ignore'):
        return np.where(z >= 0,
                        1 / (1 + np.exp(-z)),
                        np.exp(z) / (1 + np.exp(z)))

Example 15

View license
    def __init__(self, *args, **kwargs):
        super(PlotUI, self).__init__(*args, **kwargs)
        # FIXME: 'with' wrapping is temporary fix for infinite range in initial 
        # color map, which can cause a distracting warning print. This 'with'
        # wrapping should be unnecessary after fix in color_mapper.py.
        with errstate(invalid='ignore'):
            self.create_plot()

Example 16

Project: PyFNND
Source File: _fnndeconv.py
View license
def _post_LL(n_hat, res, scale_var, lD, z):

    # barrier term
    with np.errstate(invalid='ignore'):     # suppress log(0) error messages
        barrier = np.log(n_hat).sum()       # this is currently a bottleneck

    # sum of squared (predicted - actual) fluorescence
    res_ss = res.ravel().dot(res.ravel())   # fast sum-of-squares

    # weighted posterior log-likelihood of the fluorescence
    LL = -(scale_var * res_ss) - (n_hat.sum() / lD) + (z * barrier)

    return LL

Example 17

Project: hyperspy
Source File: model1d.py
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    def _poisson_likelihood_function(self, param, y, weights=None):
        """Returns the likelihood function of the model for the given
        data and parameters
        """
        mf = self._model_function(param)
        with np.errstate(invalid='ignore'):
            return -(y * np.log(mf) - mf).sum()

Example 18

Project: flopy
Source File: netcdf.py
View license
    def __truediv__(self,other):
        new_net = NetCdf.zeros_like(self)
        with np.errstate(invalid="ignore"):
            if np.isscalar(other) or isinstance(other,np.ndarray):
                for vname in self.var_attr_dict.keys():
                    new_net.nc.variables[vname][:] = self.nc.variables[vname][:] /\
                                                     other
            elif isinstance(other,NetCdf):
                for vname in self.var_attr_dict.keys():
                    new_net.nc.variables[vname][:] = self.nc.variables[vname][:] /\
                                                     other.nc.variables[vname][:]
            else:
                raise Exception("NetCdf.__sub__(): unrecognized other:{0}".\
                                format(str(type(other))))
            return new_net

Example 19

Project: flopy
Source File: netcdf.py
View license
    def __truediv__(self,other):
        new_net = NetCdf.zeros_like(self)
        with np.errstate(invalid="ignore"):
            if np.isscalar(other) or isinstance(other,np.ndarray):
                for vname in self.var_attr_dict.keys():
                    new_net.nc.variables[vname][:] = self.nc.variables[vname][:] /\
                                                     other
            elif isinstance(other,NetCdf):
                for vname in self.var_attr_dict.keys():
                    new_net.nc.variables[vname][:] = self.nc.variables[vname][:] /\
                                                     other.nc.variables[vname][:]
            else:
                raise Exception("NetCdf.__sub__(): unrecognized other:{0}".\
                                format(str(type(other))))
            return new_net

Example 20

View license
    @dec.skipif(platform.machine() == "armv5tel", "See gh-413.")
    def test_invalid(self):
        with np.errstate(all='raise', under='ignore'):
            a = -np.arange(3)
            # This should work
            with np.errstate(invalid='ignore'):
                np.sqrt(a)
            # While this should fail!
            try:
                np.sqrt(a)
            except FloatingPointError:
                pass
            else:
                self.fail("Did not raise an invalid error")

Example 21

View license
    def test_divide(self):
        with np.errstate(all='raise', under='ignore'):
            a = -np.arange(3)
            # This should work
            with np.errstate(divide='ignore'):
                a // 0
            # While this should fail!
            try:
                a // 0
            except FloatingPointError:
                pass
            else:
                self.fail("Did not raise divide by zero error")

Example 22

View license
    @dec.skipif(platform.machine() == "armv5tel", "See gh-413.")
    def test_invalid(self):
        with np.errstate(all='raise', under='ignore'):
            a = -np.arange(3)
            # This should work
            with np.errstate(invalid='ignore'):
                np.sqrt(a)
            # While this should fail!
            try:
                np.sqrt(a)
            except FloatingPointError:
                pass
            else:
                self.fail("Did not raise an invalid error")

Example 23

View license
    def test_divide(self):
        with np.errstate(all='raise', under='ignore'):
            a = -np.arange(3)
            # This should work
            with np.errstate(divide='ignore'):
                a // 0
            # While this should fail!
            try:
                a // 0
            except FloatingPointError:
                pass
            else:
                self.fail("Did not raise divide by zero error")

Example 24

View license
    def test_underlow(self):
        # Regression test for #759:
        # instanciating MachAr for dtype = np.float96 raises spurious warning.
        with errstate(all='raise'):
            try:
                self._run_machar_highprec()
            except FloatingPointError as e:
                self.fail("Caught %s exception, should not have been raised." % e)

Example 25

View license
    def test_underlow(self):
        # Regression test for #759:
        # instanciating MachAr for dtype = np.float96 raises spurious warning.
        with errstate(all='raise'):
            try:
                self._run_machar_highprec()
            except FloatingPointError as e:
                self.fail("Caught %s exception, should not have been raised." % e)

Example 26

View license
    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 27

View license
    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 28

View license
    def test_generic(self):
        with np.errstate(divide='ignore', invalid='ignore'):
            vals = isposinf(np.array((-1., 0, 1))/0.)
        assert_(vals[0] == 0)
        assert_(vals[1] == 0)
        assert_(vals[2] == 1)

Example 29

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    def test_generic(self):
        with np.errstate(divide='ignore', invalid='ignore'):
            vals = isneginf(np.array((-1., 0, 1))/0.)
        assert_(vals[0] == 1)
        assert_(vals[1] == 0)
        assert_(vals[2] == 0)

Example 30

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    def test_generic(self):
        with np.errstate(divide='ignore', invalid='ignore'):
            vals = nan_to_num(np.array((-1., 0, 1))/0.)
        assert_all(vals[0] < -1e10) and assert_all(np.isfinite(vals[0]))
        assert_(vals[1] == 0)
        assert_all(vals[2] > 1e10) and assert_all(np.isfinite(vals[2]))

Example 31

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    def test_complex_bad(self):
        with np.errstate(divide='ignore', invalid='ignore'):
            v = 1 + 1j
            v += np.array(0+1.j)/0.
        vals = nan_to_num(v)
        # !! This is actually (unexpectedly) zero
        assert_all(np.isfinite(vals))

Example 32

View license
    def test_complex_bad2(self):
        with np.errstate(divide='ignore', invalid='ignore'):
            v = 1 + 1j
            v += np.array(-1+1.j)/0.
        vals = nan_to_num(v)
        assert_all(np.isfinite(vals))

Example 33

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    def test_generic(self):
        with np.errstate(divide='ignore', invalid='ignore'):
            vals = isposinf(np.array((-1., 0, 1))/0.)
        assert_(vals[0] == 0)
        assert_(vals[1] == 0)
        assert_(vals[2] == 1)

Example 34

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    def test_generic(self):
        with np.errstate(divide='ignore', invalid='ignore'):
            vals = isneginf(np.array((-1., 0, 1))/0.)
        assert_(vals[0] == 1)
        assert_(vals[1] == 0)
        assert_(vals[2] == 0)

Example 35

View license
    def test_generic(self):
        with np.errstate(divide='ignore', invalid='ignore'):
            vals = nan_to_num(np.array((-1., 0, 1))/0.)
        assert_all(vals[0] < -1e10) and assert_all(np.isfinite(vals[0]))
        assert_(vals[1] == 0)
        assert_all(vals[2] > 1e10) and assert_all(np.isfinite(vals[2]))

Example 36

View license
    def test_complex_bad(self):
        with np.errstate(divide='ignore', invalid='ignore'):
            v = 1 + 1j
            v += np.array(0+1.j)/0.
        vals = nan_to_num(v)
        # !! This is actually (unexpectedly) zero
        assert_all(np.isfinite(vals))

Example 37

View license
    def test_complex_bad2(self):
        with np.errstate(divide='ignore', invalid='ignore'):
            v = 1 + 1j
            v += np.array(-1+1.j)/0.
        vals = nan_to_num(v)
        assert_all(np.isfinite(vals))

Example 38

View license
    def test_testScalarArithmetic(self):
        xm = array(0, mask=1)
        #TODO FIXME: Find out what the following raises a warning in r8247
        with np.errstate(divide='ignore'):
            self.assertTrue((1 / array(0)).mask)
        self.assertTrue((1 + xm).mask)
        self.assertTrue((-xm).mask)
        self.assertTrue((-xm).mask)
        self.assertTrue(maximum(xm, xm).mask)
        self.assertTrue(minimum(xm, xm).mask)
        self.assertTrue(xm.filled().dtype is xm._data.dtype)
        x = array(0, mask=0)
        self.assertTrue(x.filled() == x._data)
        self.assertEqual(str(xm), str(masked_print_option))

Example 39

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    def test_masked_unary_operations(self):
        # Tests masked_unary_operation
        (x, mx) = self.data
        with np.errstate(divide='ignore'):
            self.assertTrue(isinstance(log(mx), mmatrix))
            assert_equal(log(x), np.log(x))

Example 40

View license
    def test_testScalarArithmetic(self):
        xm = array(0, mask=1)
        #TODO FIXME: Find out what the following raises a warning in r8247
        with np.errstate(divide='ignore'):
            self.assertTrue((1 / array(0)).mask)
        self.assertTrue((1 + xm).mask)
        self.assertTrue((-xm).mask)
        self.assertTrue((-xm).mask)
        self.assertTrue(maximum(xm, xm).mask)
        self.assertTrue(minimum(xm, xm).mask)
        self.assertTrue(xm.filled().dtype is xm._data.dtype)
        x = array(0, mask=0)
        self.assertTrue(x.filled() == x._data)
        self.assertEqual(str(xm), str(masked_print_option))

Example 41

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    def test_masked_unary_operations(self):
        # Tests masked_unary_operation
        (x, mx) = self.data
        with np.errstate(divide='ignore'):
            self.assertTrue(isinstance(log(mx), mmatrix))
            assert_equal(log(x), np.log(x))

Example 42

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def _quadmin(a, fa, fpa, b, fb):
    """
    Finds the minimizer for a quadratic polynomial that goes through
    the points (a,fa), (b,fb) with derivative at a of fpa,

    """
    # f(x) = B*(x-a)^2 + C*(x-a) + D
    with np.errstate(divide='raise', over='raise', invalid='raise'):
        try:
            D = fa
            C = fpa
            db = b - a * 1.0
            B = (fb - D - C * db) / (db * db)
            xmin = a - C / (2.0 * B)
        except ArithmeticError:
            return None
    if not np.isfinite(xmin):
        return None
    return xmin

Example 43

View license
    def test_minimize_bounded_approximated(self):
        # Minimize, method='SLSQP': bounded, approximated jacobian.
        with np.errstate(invalid='ignore'):
            res = minimize(self.fun, [-1.0, 1.0], args=(-1.0, ),
                           bounds=((2.5, None), (None, 0.5)),
                           method='SLSQP', options=self.opts)
        assert_(res['success'], res['message'])
        assert_allclose(res.x, [2.5, 0.5])
        assert_(2.5 <= res.x[0])
        assert_(res.x[1] <= 0.5)

Example 44

View license
def _quadmin(a, fa, fpa, b, fb):
    """
    Finds the minimizer for a quadratic polynomial that goes through
    the points (a,fa), (b,fb) with derivative at a of fpa,

    """
    # f(x) = B*(x-a)^2 + C*(x-a) + D
    with np.errstate(divide='raise', over='raise', invalid='raise'):
        try:
            D = fa
            C = fpa
            db = b - a * 1.0
            B = (fb - D - C * db) / (db * db)
            xmin = a - C / (2.0 * B)
        except ArithmeticError:
            return None
    if not np.isfinite(xmin):
        return None
    return xmin

Example 45

View license
    def test_minimize_bounded_approximated(self):
        # Minimize, method='SLSQP': bounded, approximated jacobian.
        with np.errstate(invalid='ignore'):
            res = minimize(self.fun, [-1.0, 1.0], args=(-1.0, ),
                           bounds=((2.5, None), (None, 0.5)),
                           method='SLSQP', options=self.opts)
        assert_(res['success'], res['message'])
        assert_allclose(res.x, [2.5, 0.5])
        assert_(2.5 <= res.x[0])
        assert_(res.x[1] <= 0.5)

Example 46

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def check_probability(name):
    # Predict probabilities.
    ForestClassifier = FOREST_CLASSIFIERS[name]
    with np.errstate(divide="ignore"):
        clf = ForestClassifier(n_estimators=10, random_state=1, max_features=1,
                               max_depth=1)
        clf.fit(iris.data, iris.target)
        assert_array_almost_equal(np.sum(clf.predict_proba(iris.data), axis=1),
                                  np.ones(iris.data.shape[0]))
        assert_array_almost_equal(clf.predict_proba(iris.data),
                                  np.exp(clf.predict_log_proba(iris.data)))

Example 47

View license
def check_probability(name):
    # Predict probabilities.
    ForestClassifier = FOREST_CLASSIFIERS[name]
    with np.errstate(divide="ignore"):
        clf = ForestClassifier(n_estimators=10, random_state=1, max_features=1,
                               max_depth=1)
        clf.fit(iris.data, iris.target)
        assert_array_almost_equal(np.sum(clf.predict_proba(iris.data), axis=1),
                                  np.ones(iris.data.shape[0]))
        assert_array_almost_equal(clf.predict_proba(iris.data),
                                  np.exp(clf.predict_log_proba(iris.data)))

Example 48

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def test_numerical_stability_large_gradient():
    # Non regression test case for numerical stability on scaled problems
    # where the gradient can still explode with some losses
    model = SGDClassifier(loss='squared_hinge', n_iter=10, shuffle=True,
                          penalty='elasticnet', l1_ratio=0.3, alpha=0.01,
                          eta0=0.001, random_state=0)
    with np.errstate(all='raise'):
        model.fit(iris.data, iris.target)
    assert_true(np.isfinite(model.coef_).all())

Example 49

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def test_large_regularization():
    # Non regression tests for numerical stability issues caused by large
    # regularization parameters
    for penalty in ['l2', 'l1', 'elasticnet']:
        model = SGDClassifier(alpha=1e5, learning_rate='constant', eta0=0.1,
                              n_iter=5, penalty=penalty, shuffle=False)
        with np.errstate(all='raise'):
            model.fit(iris.data, iris.target)
        assert_array_almost_equal(model.coef_, np.zeros_like(model.coef_))

Example 50

View license
def test_numerical_stability_large_gradient():
    # Non regression test case for numerical stability on scaled problems
    # where the gradient can still explode with some losses
    model = SGDClassifier(loss='squared_hinge', n_iter=10, shuffle=True,
                          penalty='elasticnet', l1_ratio=0.3, alpha=0.01,
                          eta0=0.001, random_state=0)
    with np.errstate(all='raise'):
        model.fit(iris.data, iris.target)
    assert_true(np.isfinite(model.coef_).all())