Python numpy 模块,nextafter() 实例源码

我们从Python开源项目中,提取了以下48个代码示例,用于说明如何使用numpy.nextafter()

项目:radar    作者:amoose136    | 项目源码 | 文件源码
def test_spacing_nextafter(self):
        """Test np.spacing and np.nextafter"""
        # All non-negative finite #'s
        a = np.arange(0x7c00, dtype=uint16)
        hinf = np.array((np.inf,), dtype=float16)
        a_f16 = a.view(dtype=float16)

        assert_equal(np.spacing(a_f16[:-1]), a_f16[1:]-a_f16[:-1])

        assert_equal(np.nextafter(a_f16[:-1], hinf), a_f16[1:])
        assert_equal(np.nextafter(a_f16[0], -hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], -hinf), a_f16[:-1])

        # switch to negatives
        a |= 0x8000

        assert_equal(np.spacing(a_f16[0]), np.spacing(a_f16[1]))
        assert_equal(np.spacing(a_f16[1:]), a_f16[:-1]-a_f16[1:])

        assert_equal(np.nextafter(a_f16[0], hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], hinf), a_f16[:-1])
        assert_equal(np.nextafter(a_f16[:-1], -hinf), a_f16[1:])
项目:zhusuan    作者:thu-ml    | 项目源码 | 文件源码
def _sample(self, n_samples):
        # samples must be sampled from (-1, 1) rather than [-1, 1)
        loc, scale = self.loc, self.scale
        if not self.is_reparameterized:
            loc = tf.stop_gradient(loc)
            scale = tf.stop_gradient(scale)
        shape = tf.concat([[n_samples], self.batch_shape], 0)
        uniform_samples = tf.random_uniform(
            shape=shape,
            minval=np.nextafter(self.dtype.as_numpy_dtype(-1.),
                                self.dtype.as_numpy_dtype(0.)),
            maxval=1.,
            dtype=self.dtype)
        samples = loc - scale * tf.sign(uniform_samples) * \
            tf.log1p(-tf.abs(uniform_samples))
        static_n_samples = n_samples if isinstance(n_samples, int) else None
        samples.set_shape(
            tf.TensorShape([static_n_samples]).concatenate(
                self.get_batch_shape()))
        return samples
项目:krpcScripts    作者:jwvanderbeck    | 项目源码 | 文件源码
def test_spacing_nextafter(self):
        """Test np.spacing and np.nextafter"""
        # All non-negative finite #'s
        a = np.arange(0x7c00, dtype=uint16)
        hinf = np.array((np.inf,), dtype=float16)
        a_f16 = a.view(dtype=float16)

        assert_equal(np.spacing(a_f16[:-1]), a_f16[1:]-a_f16[:-1])

        assert_equal(np.nextafter(a_f16[:-1], hinf), a_f16[1:])
        assert_equal(np.nextafter(a_f16[0], -hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], -hinf), a_f16[:-1])

        # switch to negatives
        a |= 0x8000

        assert_equal(np.spacing(a_f16[0]), np.spacing(a_f16[1]))
        assert_equal(np.spacing(a_f16[1:]), a_f16[:-1]-a_f16[1:])

        assert_equal(np.nextafter(a_f16[0], hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], hinf), a_f16[:-1])
        assert_equal(np.nextafter(a_f16[:-1], -hinf), a_f16[1:])
项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda    作者:SignalMedia    | 项目源码 | 文件源码
def test_spacing_nextafter(self):
        """Test np.spacing and np.nextafter"""
        # All non-negative finite #'s
        a = np.arange(0x7c00, dtype=uint16)
        hinf = np.array((np.inf,), dtype=float16)
        a_f16 = a.view(dtype=float16)

        assert_equal(np.spacing(a_f16[:-1]), a_f16[1:]-a_f16[:-1])

        assert_equal(np.nextafter(a_f16[:-1], hinf), a_f16[1:])
        assert_equal(np.nextafter(a_f16[0], -hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], -hinf), a_f16[:-1])

        # switch to negatives
        a |= 0x8000

        assert_equal(np.spacing(a_f16[0]), np.spacing(a_f16[1]))
        assert_equal(np.spacing(a_f16[1:]), a_f16[:-1]-a_f16[1:])

        assert_equal(np.nextafter(a_f16[0], hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], hinf), a_f16[:-1])
        assert_equal(np.nextafter(a_f16[:-1], -hinf), a_f16[1:])
项目:aws-lambda-numpy    作者:vitolimandibhrata    | 项目源码 | 文件源码
def test_spacing_nextafter(self):
        """Test np.spacing and np.nextafter"""
        # All non-negative finite #'s
        a = np.arange(0x7c00, dtype=uint16)
        hinf = np.array((np.inf,), dtype=float16)
        a_f16 = a.view(dtype=float16)

        assert_equal(np.spacing(a_f16[:-1]), a_f16[1:]-a_f16[:-1])

        assert_equal(np.nextafter(a_f16[:-1], hinf), a_f16[1:])
        assert_equal(np.nextafter(a_f16[0], -hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], -hinf), a_f16[:-1])

        # switch to negatives
        a |= 0x8000

        assert_equal(np.spacing(a_f16[0]), np.spacing(a_f16[1]))
        assert_equal(np.spacing(a_f16[1:]), a_f16[:-1]-a_f16[1:])

        assert_equal(np.nextafter(a_f16[0], hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], hinf), a_f16[:-1])
        assert_equal(np.nextafter(a_f16[:-1], -hinf), a_f16[1:])
项目:lambda-numba    作者:rlhotovy    | 项目源码 | 文件源码
def test_spacing_nextafter(self):
        """Test np.spacing and np.nextafter"""
        # All non-negative finite #'s
        a = np.arange(0x7c00, dtype=uint16)
        hinf = np.array((np.inf,), dtype=float16)
        a_f16 = a.view(dtype=float16)

        assert_equal(np.spacing(a_f16[:-1]), a_f16[1:]-a_f16[:-1])

        assert_equal(np.nextafter(a_f16[:-1], hinf), a_f16[1:])
        assert_equal(np.nextafter(a_f16[0], -hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], -hinf), a_f16[:-1])

        # switch to negatives
        a |= 0x8000

        assert_equal(np.spacing(a_f16[0]), np.spacing(a_f16[1]))
        assert_equal(np.spacing(a_f16[1:]), a_f16[:-1]-a_f16[1:])

        assert_equal(np.nextafter(a_f16[0], hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], hinf), a_f16[:-1])
        assert_equal(np.nextafter(a_f16[:-1], -hinf), a_f16[1:])
项目:deliver    作者:orchestor    | 项目源码 | 文件源码
def test_spacing_nextafter(self):
        """Test np.spacing and np.nextafter"""
        # All non-negative finite #'s
        a = np.arange(0x7c00, dtype=uint16)
        hinf = np.array((np.inf,), dtype=float16)
        a_f16 = a.view(dtype=float16)

        assert_equal(np.spacing(a_f16[:-1]), a_f16[1:]-a_f16[:-1])

        assert_equal(np.nextafter(a_f16[:-1], hinf), a_f16[1:])
        assert_equal(np.nextafter(a_f16[0], -hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], -hinf), a_f16[:-1])

        # switch to negatives
        a |= 0x8000

        assert_equal(np.spacing(a_f16[0]), np.spacing(a_f16[1]))
        assert_equal(np.spacing(a_f16[1:]), a_f16[:-1]-a_f16[1:])

        assert_equal(np.nextafter(a_f16[0], hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], hinf), a_f16[:-1])
        assert_equal(np.nextafter(a_f16[:-1], -hinf), a_f16[1:])
项目:Parallel-SGD    作者:angadgill    | 项目源码 | 文件源码
def test_standard_scaler_trasform_with_partial_fit():
    # Check some postconditions after applying partial_fit and transform
    X = X_2d[:100, :]

    scaler_incr = StandardScaler()
    for i, batch in enumerate(gen_batches(X.shape[0], 1)):

        X_sofar = X[:(i + 1), :]
        chunks_copy = X_sofar.copy()
        scaled_batch = StandardScaler().fit_transform(X_sofar)

        scaler_incr = scaler_incr.partial_fit(X[batch])
        scaled_incr = scaler_incr.transform(X_sofar)

        assert_array_almost_equal(scaled_batch, scaled_incr)
        assert_array_almost_equal(X_sofar, chunks_copy)  # No change
        right_input = scaler_incr.inverse_transform(scaled_incr)
        assert_array_almost_equal(X_sofar, right_input)

        zero = np.zeros(X.shape[1])
        epsilon = np.nextafter(0, 1)
        assert_array_less(zero, scaler_incr.var_ + epsilon)  # as less or equal
        assert_array_less(zero, scaler_incr.scale_ + epsilon)
        # (i+1) because the Scaler has been already fitted
        assert_equal((i + 1), scaler_incr.n_samples_seen_)
项目:DeepLearning_VirtualReality_BigData_Project    作者:rashmitripathi    | 项目源码 | 文件源码
def _sample_n(self, n, seed=None):
    sample_shape = array_ops.concat(([n], array_ops.shape(self.logits)), 0)
    logits = self.logits * array_ops.ones(sample_shape)
    if logits.get_shape().ndims == 2:
      logits_2d = logits
    else:
      logits_2d = array_ops.reshape(logits, [-1, self.event_size])
    np_dtype = self.dtype.as_numpy_dtype()
    minval = np.nextafter(np_dtype(0), np_dtype(1))
    uniform = random_ops.random_uniform(shape=array_ops.shape(logits_2d),
                                        minval=minval,
                                        maxval=1,
                                        dtype=self.dtype,
                                        seed=seed)
    gumbel = - math_ops.log(- math_ops.log(uniform))
    noisy_logits = math_ops.div(gumbel + logits_2d, self.temperature)
    samples = nn_ops.log_softmax(noisy_logits)
    ret = array_ops.reshape(samples, sample_shape)
    return ret
项目:Alfred    作者:jkachhadia    | 项目源码 | 文件源码
def test_spacing_nextafter(self):
        """Test np.spacing and np.nextafter"""
        # All non-negative finite #'s
        a = np.arange(0x7c00, dtype=uint16)
        hinf = np.array((np.inf,), dtype=float16)
        a_f16 = a.view(dtype=float16)

        assert_equal(np.spacing(a_f16[:-1]), a_f16[1:]-a_f16[:-1])

        assert_equal(np.nextafter(a_f16[:-1], hinf), a_f16[1:])
        assert_equal(np.nextafter(a_f16[0], -hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], -hinf), a_f16[:-1])

        # switch to negatives
        a |= 0x8000

        assert_equal(np.spacing(a_f16[0]), np.spacing(a_f16[1]))
        assert_equal(np.spacing(a_f16[1:]), a_f16[:-1]-a_f16[1:])

        assert_equal(np.nextafter(a_f16[0], hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], hinf), a_f16[:-1])
        assert_equal(np.nextafter(a_f16[:-1], -hinf), a_f16[1:])
项目:radar    作者:amoose136    | 项目源码 | 文件源码
def test_float_remainder_corner_cases(self):
        # Check remainder magnitude.
        for dt in np.typecodes['Float']:
            b = np.array(1.0, dtype=dt)
            a = np.nextafter(np.array(0.0, dtype=dt), -b)
            rem = np.remainder(a, b)
            assert_(rem <= b, 'dt: %s' % dt)
            rem = np.remainder(-a, -b)
            assert_(rem >= -b, 'dt: %s' % dt)

        # Check nans, inf
        with warnings.catch_warnings():
            warnings.simplefilter('always')
            warnings.simplefilter('ignore', RuntimeWarning)
            for dt in np.typecodes['Float']:
                fone = np.array(1.0, dtype=dt)
                fzer = np.array(0.0, dtype=dt)
                finf = np.array(np.inf, dtype=dt)
                fnan = np.array(np.nan, dtype=dt)
                rem = np.remainder(fone, fzer)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
                # MSVC 2008 returns NaN here, so disable the check.
                #rem = np.remainder(fone, finf)
                #assert_(rem == fone, 'dt: %s, rem: %s' % (dt, rem))
                rem = np.remainder(fone, fnan)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
                rem = np.remainder(finf, fone)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
项目:radar    作者:amoose136    | 项目源码 | 文件源码
def _test_nextafter(t):
    one = t(1)
    two = t(2)
    zero = t(0)
    eps = np.finfo(t).eps
    assert_(np.nextafter(one, two) - one == eps)
    assert_(np.nextafter(one, zero) - one < 0)
    assert_(np.isnan(np.nextafter(np.nan, one)))
    assert_(np.isnan(np.nextafter(one, np.nan)))
    assert_(np.nextafter(one, one) == one)
项目:radar    作者:amoose136    | 项目源码 | 文件源码
def test_nextafter_vs_spacing():
    # XXX: spacing does not handle long double yet
    for t in [np.float32, np.float64]:
        for _f in [1, 1e-5, 1000]:
            f = t(_f)
            f1 = t(_f + 1)
            assert_(np.nextafter(f, f1) - f == np.spacing(f))
项目:radar    作者:amoose136    | 项目源码 | 文件源码
def test_float_modulus_corner_cases(self):
        # Check remainder magnitude.
        for dt in np.typecodes['Float']:
            b = np.array(1.0, dtype=dt)
            a = np.nextafter(np.array(0.0, dtype=dt), -b)
            rem = self.mod(a, b)
            assert_(rem <= b, 'dt: %s' % dt)
            rem = self.mod(-a, -b)
            assert_(rem >= -b, 'dt: %s' % dt)

        # Check nans, inf
        with warnings.catch_warnings():
            warnings.simplefilter('always')
            warnings.simplefilter('ignore', RuntimeWarning)
            for dt in np.typecodes['Float']:
                fone = np.array(1.0, dtype=dt)
                fzer = np.array(0.0, dtype=dt)
                finf = np.array(np.inf, dtype=dt)
                fnan = np.array(np.nan, dtype=dt)
                rem = self.mod(fone, fzer)
                assert_(np.isnan(rem), 'dt: %s' % dt)
                # MSVC 2008 returns NaN here, so disable the check.
                #rem = self.mod(fone, finf)
                #assert_(rem == fone, 'dt: %s' % dt)
                rem = self.mod(fone, fnan)
                assert_(np.isnan(rem), 'dt: %s' % dt)
                rem = self.mod(finf, fone)
                assert_(np.isnan(rem), 'dt: %s' % dt)
项目:krpcScripts    作者:jwvanderbeck    | 项目源码 | 文件源码
def test_float_remainder_corner_cases(self):
        # Check remainder magnitude.
        for dt in np.typecodes['Float']:
            b = np.array(1.0, dtype=dt)
            a = np.nextafter(np.array(0.0, dtype=dt), -b)
            rem = np.remainder(a, b)
            assert_(rem <= b, 'dt: %s' % dt)
            rem = np.remainder(-a, -b)
            assert_(rem >= -b, 'dt: %s' % dt)

        # Check nans, inf
        with warnings.catch_warnings():
            warnings.simplefilter('always')
            warnings.simplefilter('ignore', RuntimeWarning)
            for dt in np.typecodes['Float']:
                fone = np.array(1.0, dtype=dt)
                fzer = np.array(0.0, dtype=dt)
                finf = np.array(np.inf, dtype=dt)
                fnan = np.array(np.nan, dtype=dt)
                rem = np.remainder(fone, fzer)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
                # MSVC 2008 returns NaN here, so disable the check.
                #rem = np.remainder(fone, finf)
                #assert_(rem == fone, 'dt: %s, rem: %s' % (dt, rem))
                rem = np.remainder(fone, fnan)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
                rem = np.remainder(finf, fone)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
项目:krpcScripts    作者:jwvanderbeck    | 项目源码 | 文件源码
def _test_nextafter(t):
    one = t(1)
    two = t(2)
    zero = t(0)
    eps = np.finfo(t).eps
    assert_(np.nextafter(one, two) - one == eps)
    assert_(np.nextafter(one, zero) - one < 0)
    assert_(np.isnan(np.nextafter(np.nan, one)))
    assert_(np.isnan(np.nextafter(one, np.nan)))
    assert_(np.nextafter(one, one) == one)
项目:krpcScripts    作者:jwvanderbeck    | 项目源码 | 文件源码
def test_nextafter_vs_spacing():
    # XXX: spacing does not handle long double yet
    for t in [np.float32, np.float64]:
        for _f in [1, 1e-5, 1000]:
            f = t(_f)
            f1 = t(_f + 1)
            assert_(np.nextafter(f, f1) - f == np.spacing(f))
项目:krpcScripts    作者:jwvanderbeck    | 项目源码 | 文件源码
def test_float_modulus_corner_cases(self):
        # Check remainder magnitude.
        for dt in np.typecodes['Float']:
            b = np.array(1.0, dtype=dt)
            a = np.nextafter(np.array(0.0, dtype=dt), -b)
            rem = self.mod(a, b)
            assert_(rem <= b, 'dt: %s' % dt)
            rem = self.mod(-a, -b)
            assert_(rem >= -b, 'dt: %s' % dt)

        # Check nans, inf
        with warnings.catch_warnings():
            warnings.simplefilter('always')
            warnings.simplefilter('ignore', RuntimeWarning)
            for dt in np.typecodes['Float']:
                fone = np.array(1.0, dtype=dt)
                fzer = np.array(0.0, dtype=dt)
                finf = np.array(np.inf, dtype=dt)
                fnan = np.array(np.nan, dtype=dt)
                rem = self.mod(fone, fzer)
                assert_(np.isnan(rem), 'dt: %s' % dt)
                # MSVC 2008 returns NaN here, so disable the check.
                #rem = self.mod(fone, finf)
                #assert_(rem == fone, 'dt: %s' % dt)
                rem = self.mod(fone, fnan)
                assert_(np.isnan(rem), 'dt: %s' % dt)
                rem = self.mod(finf, fone)
                assert_(np.isnan(rem), 'dt: %s' % dt)
项目:TX-Means    作者:riccotti    | 项目源码 | 文件源码
def loglikelihood(num_points, num_dims, clusters, distances_dict_values_cl):
    ll = 0
    variance = cluster_variance(num_points, clusters, distances_dict_values_cl) or np.nextafter(0, 1)
    # print 'var', variance
    for cluster in clusters:
        fRn = len(cluster)
        t1 = fRn * np.log(fRn)
        t2 = fRn * np.log(num_points)
        t3 = ((fRn * num_dims) / 2.0) * np.log((2.0 * np.pi) * variance)
        t4 = (fRn - 1.0) / 2.0
        ll += t1 - t2 - t3 - t4
    return ll
项目:lsdc    作者:febert    | 项目源码 | 文件源码
def _sample_n(self, n, seed=None):
    shape = array_ops.concat(0, ([n], self.batch_shape()))
    # Sample uniformly-at-random from the open-interval (-1, 1).
    uniform_samples = random_ops.random_uniform(
        shape=shape,
        minval=np.nextafter(self.dtype.as_numpy_dtype(-1.),
                            self.dtype.as_numpy_dtype(0.)),
        maxval=1.,
        dtype=self.dtype,
        seed=seed)
    return (self.loc - self.scale * math_ops.sign(uniform_samples) *
            math_ops.log(1. - math_ops.abs(uniform_samples)))
项目:lsdc    作者:febert    | 项目源码 | 文件源码
def _sample_n(self, n, seed=None):
    shape = array_ops.concat(0, ([n], array_ops.shape(self._lam)))
    # Sample uniformly-at-random from the open-interval (0, 1).
    sampled = random_ops.random_uniform(
        shape,
        minval=np.nextafter(self.dtype.as_numpy_dtype(0.),
                            self.dtype.as_numpy_dtype(1.)),
        maxval=array_ops.ones((), dtype=self.dtype),
        seed=seed,
        dtype=self.dtype)
    return -math_ops.log(sampled) / self._lam
项目:lsdc    作者:febert    | 项目源码 | 文件源码
def _sample_n(self, n, seed=None):
    shape = array_ops.concat(0, ([n], self.batch_shape()))
    # Sample uniformly-at-random from the open-interval (-1, 1).
    uniform_samples = random_ops.random_uniform(
        shape=shape,
        minval=np.nextafter(self.dtype.as_numpy_dtype(-1.),
                            self.dtype.as_numpy_dtype(0.)),
        maxval=1.,
        dtype=self.dtype,
        seed=seed)
    return (self.loc - self.scale * math_ops.sign(uniform_samples) *
            math_ops.log(1. - math_ops.abs(uniform_samples)))
项目:lsdc    作者:febert    | 项目源码 | 文件源码
def _sample_n(self, n, seed=None):
    shape = array_ops.concat(0, ([n], array_ops.shape(self._lam)))
    # Sample uniformly-at-random from the open-interval (0, 1).
    sampled = random_ops.random_uniform(
        shape,
        minval=np.nextafter(self.dtype.as_numpy_dtype(0.),
                            self.dtype.as_numpy_dtype(1.)),
        maxval=array_ops.ones((), dtype=self.dtype),
        seed=seed,
        dtype=self.dtype)
    return -math_ops.log(sampled) / self._lam
项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda    作者:SignalMedia    | 项目源码 | 文件源码
def _test_nextafter(t):
    one = t(1)
    two = t(2)
    zero = t(0)
    eps = np.finfo(t).eps
    assert_(np.nextafter(one, two) - one == eps)
    assert_(np.nextafter(one, zero) - one < 0)
    assert_(np.isnan(np.nextafter(np.nan, one)))
    assert_(np.isnan(np.nextafter(one, np.nan)))
    assert_(np.nextafter(one, one) == one)
项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda    作者:SignalMedia    | 项目源码 | 文件源码
def test_nextafter_vs_spacing():
    # XXX: spacing does not handle long double yet
    for t in [np.float32, np.float64]:
        for _f in [1, 1e-5, 1000]:
            f = t(_f)
            f1 = t(_f + 1)
            assert_(np.nextafter(f, f1) - f == np.spacing(f))
项目:aws-lambda-numpy    作者:vitolimandibhrata    | 项目源码 | 文件源码
def _test_nextafter(t):
    one = t(1)
    two = t(2)
    zero = t(0)
    eps = np.finfo(t).eps
    assert_(np.nextafter(one, two) - one == eps)
    assert_(np.nextafter(one, zero) - one < 0)
    assert_(np.isnan(np.nextafter(np.nan, one)))
    assert_(np.isnan(np.nextafter(one, np.nan)))
    assert_(np.nextafter(one, one) == one)
项目:aws-lambda-numpy    作者:vitolimandibhrata    | 项目源码 | 文件源码
def test_nextafter_vs_spacing():
    # XXX: spacing does not handle long double yet
    for t in [np.float32, np.float64]:
        for _f in [1, 1e-5, 1000]:
            f = t(_f)
            f1 = t(_f + 1)
            assert_(np.nextafter(f, f1) - f == np.spacing(f))
项目:scipy-2017-cython-tutorial    作者:kwmsmith    | 项目源码 | 文件源码
def test_uniform_range_bounds(self):
        fmin = np.finfo('float').min
        fmax = np.finfo('float').max

        func = mt19937.uniform
        assert_raises(OverflowError, func, -np.inf, 0)
        assert_raises(OverflowError, func,  0,      np.inf)
        assert_raises(OverflowError, func,  fmin,   fmax)
        assert_raises(OverflowError, func, [-np.inf], [0])
        assert_raises(OverflowError, func, [0], [np.inf])

        # (fmax / 1e17) - fmin is within range, so this should not throw
        # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >
        # DBL_MAX by increasing fmin a bit
        mt19937.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)
项目:lambda-numba    作者:rlhotovy    | 项目源码 | 文件源码
def test_float_remainder_corner_cases(self):
        # Check remainder magnitude.
        for dt in np.typecodes['Float']:
            b = np.array(1.0, dtype=dt)
            a = np.nextafter(np.array(0.0, dtype=dt), -b)
            rem = np.remainder(a, b)
            assert_(rem <= b, 'dt: %s' % dt)
            rem = np.remainder(-a, -b)
            assert_(rem >= -b, 'dt: %s' % dt)

        # Check nans, inf
        with warnings.catch_warnings():
            warnings.simplefilter('always')
            warnings.simplefilter('ignore', RuntimeWarning)
            for dt in np.typecodes['Float']:
                fone = np.array(1.0, dtype=dt)
                fzer = np.array(0.0, dtype=dt)
                finf = np.array(np.inf, dtype=dt)
                fnan = np.array(np.nan, dtype=dt)
                rem = np.remainder(fone, fzer)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
                # MSVC 2008 returns NaN here, so disable the check.
                #rem = np.remainder(fone, finf)
                #assert_(rem == fone, 'dt: %s, rem: %s' % (dt, rem))
                rem = np.remainder(fone, fnan)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
                rem = np.remainder(finf, fone)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
项目:lambda-numba    作者:rlhotovy    | 项目源码 | 文件源码
def _test_nextafter(t):
    one = t(1)
    two = t(2)
    zero = t(0)
    eps = np.finfo(t).eps
    assert_(np.nextafter(one, two) - one == eps)
    assert_(np.nextafter(one, zero) - one < 0)
    assert_(np.isnan(np.nextafter(np.nan, one)))
    assert_(np.isnan(np.nextafter(one, np.nan)))
    assert_(np.nextafter(one, one) == one)
项目:lambda-numba    作者:rlhotovy    | 项目源码 | 文件源码
def test_nextafter_vs_spacing():
    # XXX: spacing does not handle long double yet
    for t in [np.float32, np.float64]:
        for _f in [1, 1e-5, 1000]:
            f = t(_f)
            f1 = t(_f + 1)
            assert_(np.nextafter(f, f1) - f == np.spacing(f))
项目:lambda-numba    作者:rlhotovy    | 项目源码 | 文件源码
def test_float_modulus_corner_cases(self):
        # Check remainder magnitude.
        for dt in np.typecodes['Float']:
            b = np.array(1.0, dtype=dt)
            a = np.nextafter(np.array(0.0, dtype=dt), -b)
            rem = self.mod(a, b)
            assert_(rem <= b, 'dt: %s' % dt)
            rem = self.mod(-a, -b)
            assert_(rem >= -b, 'dt: %s' % dt)

        # Check nans, inf
        with warnings.catch_warnings():
            warnings.simplefilter('always')
            warnings.simplefilter('ignore', RuntimeWarning)
            for dt in np.typecodes['Float']:
                fone = np.array(1.0, dtype=dt)
                fzer = np.array(0.0, dtype=dt)
                finf = np.array(np.inf, dtype=dt)
                fnan = np.array(np.nan, dtype=dt)
                rem = self.mod(fone, fzer)
                assert_(np.isnan(rem), 'dt: %s' % dt)
                # MSVC 2008 returns NaN here, so disable the check.
                #rem = self.mod(fone, finf)
                #assert_(rem == fone, 'dt: %s' % dt)
                rem = self.mod(fone, fnan)
                assert_(np.isnan(rem), 'dt: %s' % dt)
                rem = self.mod(finf, fone)
                assert_(np.isnan(rem), 'dt: %s' % dt)
项目:deliver    作者:orchestor    | 项目源码 | 文件源码
def test_float_remainder_corner_cases(self):
        # Check remainder magnitude.
        for dt in np.typecodes['Float']:
            b = np.array(1.0, dtype=dt)
            a = np.nextafter(np.array(0.0, dtype=dt), -b)
            rem = np.remainder(a, b)
            assert_(rem <= b, 'dt: %s' % dt)
            rem = np.remainder(-a, -b)
            assert_(rem >= -b, 'dt: %s' % dt)

        # Check nans, inf
        with suppress_warnings() as sup:
            sup.filter(RuntimeWarning, "invalid value encountered in remainder")
            for dt in np.typecodes['Float']:
                fone = np.array(1.0, dtype=dt)
                fzer = np.array(0.0, dtype=dt)
                finf = np.array(np.inf, dtype=dt)
                fnan = np.array(np.nan, dtype=dt)
                rem = np.remainder(fone, fzer)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
                # MSVC 2008 returns NaN here, so disable the check.
                #rem = np.remainder(fone, finf)
                #assert_(rem == fone, 'dt: %s, rem: %s' % (dt, rem))
                rem = np.remainder(fone, fnan)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
                rem = np.remainder(finf, fone)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
项目:deliver    作者:orchestor    | 项目源码 | 文件源码
def _test_nextafter(t):
    one = t(1)
    two = t(2)
    zero = t(0)
    eps = np.finfo(t).eps
    assert_(np.nextafter(one, two) - one == eps)
    assert_(np.nextafter(one, zero) - one < 0)
    assert_(np.isnan(np.nextafter(np.nan, one)))
    assert_(np.isnan(np.nextafter(one, np.nan)))
    assert_(np.nextafter(one, one) == one)
项目:deliver    作者:orchestor    | 项目源码 | 文件源码
def test_nextafter_vs_spacing():
    # XXX: spacing does not handle long double yet
    for t in [np.float32, np.float64]:
        for _f in [1, 1e-5, 1000]:
            f = t(_f)
            f1 = t(_f + 1)
            assert_(np.nextafter(f, f1) - f == np.spacing(f))
项目:deliver    作者:orchestor    | 项目源码 | 文件源码
def test_float_modulus_corner_cases(self):
        # Check remainder magnitude.
        for dt in np.typecodes['Float']:
            b = np.array(1.0, dtype=dt)
            a = np.nextafter(np.array(0.0, dtype=dt), -b)
            rem = self.mod(a, b)
            assert_(rem <= b, 'dt: %s' % dt)
            rem = self.mod(-a, -b)
            assert_(rem >= -b, 'dt: %s' % dt)

        # Check nans, inf
        with suppress_warnings() as sup:
            sup.filter(RuntimeWarning, "invalid value encountered in remainder")
            for dt in np.typecodes['Float']:
                fone = np.array(1.0, dtype=dt)
                fzer = np.array(0.0, dtype=dt)
                finf = np.array(np.inf, dtype=dt)
                fnan = np.array(np.nan, dtype=dt)
                rem = self.mod(fone, fzer)
                assert_(np.isnan(rem), 'dt: %s' % dt)
                # MSVC 2008 returns NaN here, so disable the check.
                #rem = self.mod(fone, finf)
                #assert_(rem == fone, 'dt: %s' % dt)
                rem = self.mod(fone, fnan)
                assert_(np.isnan(rem), 'dt: %s' % dt)
                rem = self.mod(finf, fone)
                assert_(np.isnan(rem), 'dt: %s' % dt)
项目:scikit-optimize    作者:scikit-optimize    | 项目源码 | 文件源码
def _uniform_inclusive(loc=0.0, scale=1.0):
    # like scipy.stats.distributions but inclusive of `high`
    # XXX scale + 1. might not actually be a float after scale if
    # XXX scale is very large.
    return uniform(loc=loc, scale=np.nextafter(scale, scale + 1.))
项目:scikit-optimize    作者:scikit-optimize    | 项目源码 | 文件源码
def test_real_bounds():
    # should give same answer as using check_limits() but this is easier
    # to read
    a = Real(1., 2.1)
    assert_false(0.99 in a)
    assert_true(1. in a)
    assert_true(2.09 in a)
    assert_true(2.1 in a)
    assert_false(np.nextafter(2.1, 3.) in a)
项目:DeepLearning_VirtualReality_BigData_Project    作者:rashmitripathi    | 项目源码 | 文件源码
def _sample_n(self, n, seed=None):
    shape = array_ops.concat(([n], self.batch_shape()), 0)
    # Sample uniformly-at-random from the open-interval (-1, 1).
    uniform_samples = random_ops.random_uniform(
        shape=shape,
        minval=np.nextafter(self.dtype.as_numpy_dtype(-1.),
                            self.dtype.as_numpy_dtype(0.)),
        maxval=1.,
        dtype=self.dtype,
        seed=seed)
    return (self.loc - self.scale * math_ops.sign(uniform_samples) *
            math_ops.log(1. - math_ops.abs(uniform_samples)))
项目:DeepLearning_VirtualReality_BigData_Project    作者:rashmitripathi    | 项目源码 | 文件源码
def _sample_n(self, n, seed=None):
    shape = array_ops.concat(([n], array_ops.shape(self.mean())), 0)
    np_dtype = self.dtype.as_numpy_dtype()
    minval = np.nextafter(np_dtype(0), np_dtype(1))
    uniform = random_ops.random_uniform(shape=shape,
                                        minval=minval,
                                        maxval=1,
                                        dtype=self.dtype,
                                        seed=seed)
    sampled = -math_ops.log(-math_ops.log(uniform))
    return sampled * self.scale + self.loc
项目:DeepLearning_VirtualReality_BigData_Project    作者:rashmitripathi    | 项目源码 | 文件源码
def _sample_n(self, n, seed=None):
    shape = array_ops.concat(([n], array_ops.shape(self.mean())), 0)
    np_dtype = self.dtype.as_numpy_dtype()
    minval = np.nextafter(np_dtype(0), np_dtype(1))
    uniform = random_ops.random_uniform(shape=shape,
                                        minval=minval,
                                        maxval=1,
                                        dtype=self.dtype,
                                        seed=seed)
    sampled = math_ops.log(uniform) - math_ops.log(1-uniform)
    return sampled * self.scale + self.loc
项目:DeepLearning_VirtualReality_BigData_Project    作者:rashmitripathi    | 项目源码 | 文件源码
def _sample_n(self, n, seed=None):
    shape = array_ops.concat(([n], array_ops.shape(self._lam)), 0)
    # Sample uniformly-at-random from the open-interval (0, 1).
    sampled = random_ops.random_uniform(
        shape,
        minval=np.nextafter(self.dtype.as_numpy_dtype(0.),
                            self.dtype.as_numpy_dtype(1.)),
        maxval=array_ops.ones((), dtype=self.dtype),
        seed=seed,
        dtype=self.dtype)
    return -math_ops.log(sampled) / self._lam
项目:Alfred    作者:jkachhadia    | 项目源码 | 文件源码
def test_float_remainder_corner_cases(self):
        # Check remainder magnitude.
        for dt in np.typecodes['Float']:
            b = np.array(1.0, dtype=dt)
            a = np.nextafter(np.array(0.0, dtype=dt), -b)
            rem = np.remainder(a, b)
            assert_(rem <= b, 'dt: %s' % dt)
            rem = np.remainder(-a, -b)
            assert_(rem >= -b, 'dt: %s' % dt)

        # Check nans, inf
        with warnings.catch_warnings():
            warnings.simplefilter('always')
            warnings.simplefilter('ignore', RuntimeWarning)
            for dt in np.typecodes['Float']:
                fone = np.array(1.0, dtype=dt)
                fzer = np.array(0.0, dtype=dt)
                finf = np.array(np.inf, dtype=dt)
                fnan = np.array(np.nan, dtype=dt)
                rem = np.remainder(fone, fzer)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
                # MSVC 2008 returns NaN here, so disable the check.
                #rem = np.remainder(fone, finf)
                #assert_(rem == fone, 'dt: %s, rem: %s' % (dt, rem))
                rem = np.remainder(fone, fnan)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
                rem = np.remainder(finf, fone)
                assert_(np.isnan(rem), 'dt: %s, rem: %s' % (dt, rem))
项目:Alfred    作者:jkachhadia    | 项目源码 | 文件源码
def _test_nextafter(t):
    one = t(1)
    two = t(2)
    zero = t(0)
    eps = np.finfo(t).eps
    assert_(np.nextafter(one, two) - one == eps)
    assert_(np.nextafter(one, zero) - one < 0)
    assert_(np.isnan(np.nextafter(np.nan, one)))
    assert_(np.isnan(np.nextafter(one, np.nan)))
    assert_(np.nextafter(one, one) == one)
项目:Alfred    作者:jkachhadia    | 项目源码 | 文件源码
def test_nextafter_vs_spacing():
    # XXX: spacing does not handle long double yet
    for t in [np.float32, np.float64]:
        for _f in [1, 1e-5, 1000]:
            f = t(_f)
            f1 = t(_f + 1)
            assert_(np.nextafter(f, f1) - f == np.spacing(f))
项目:Alfred    作者:jkachhadia    | 项目源码 | 文件源码
def test_float_modulus_corner_cases(self):
        # Check remainder magnitude.
        for dt in np.typecodes['Float']:
            b = np.array(1.0, dtype=dt)
            a = np.nextafter(np.array(0.0, dtype=dt), -b)
            rem = self.mod(a, b)
            assert_(rem <= b, 'dt: %s' % dt)
            rem = self.mod(-a, -b)
            assert_(rem >= -b, 'dt: %s' % dt)

        # Check nans, inf
        with warnings.catch_warnings():
            warnings.simplefilter('always')
            warnings.simplefilter('ignore', RuntimeWarning)
            for dt in np.typecodes['Float']:
                fone = np.array(1.0, dtype=dt)
                fzer = np.array(0.0, dtype=dt)
                finf = np.array(np.inf, dtype=dt)
                fnan = np.array(np.nan, dtype=dt)
                rem = self.mod(fone, fzer)
                assert_(np.isnan(rem), 'dt: %s' % dt)
                # MSVC 2008 returns NaN here, so disable the check.
                #rem = self.mod(fone, finf)
                #assert_(rem == fone, 'dt: %s' % dt)
                rem = self.mod(fone, fnan)
                assert_(np.isnan(rem), 'dt: %s' % dt)
                rem = self.mod(finf, fone)
                assert_(np.isnan(rem), 'dt: %s' % dt)
项目:nengo_spa    作者:nengo    | 项目源码 | 文件源码
def similarity(data, vocab, normalize=False):
    """Return the similarity between some data and the vocabulary.

    Computes the dot products between all data vectors and each
    vocabulary vector. If ``normalize=True``, normalizes all vectors
    to compute the cosine similarity.

    Parameters
    ----------
    data: array_like
        The data used for comparison.
    vocab: Vocabulary or array_like
        Vocabulary (or list of vectors) to use to calculate
        the similarity values.
    normalize : bool, optional (Default: False)
        Whether to normalize all vectors, to compute the cosine similarity.
    """
    from nengo_spa.vocab import Vocabulary

    if isinstance(data, SemanticPointer):
        data = data.v

    if isinstance(vocab, Vocabulary):
        vectors = vocab.vectors
    elif is_iterable(vocab):
        if isinstance(next(iter(vocab)), SemanticPointer):
            vocab = [p.v for p in vocab]
        vectors = np.array(vocab, copy=False, ndmin=2)
    else:
        raise ValidationError("%r object is not a valid vocabulary"
                              % (type(vocab).__name__), attr='vocab')

    dots = np.dot(vectors, data.T)

    if normalize:
        # Zero-norm vectors should return zero, so avoid divide-by-zero error
        eps = np.nextafter(0, 1)  # smallest float above zero
        dnorm = np.maximum(npext.norm(data.T, axis=0, keepdims=True), eps)
        vnorm = np.maximum(npext.norm(vectors, axis=1, keepdims=True), eps)

        if len(dots.shape) == 1:
            vnorm = np.squeeze(vnorm)

        dots /= dnorm
        dots /= vnorm

    return dots.T
项目:Parallel-SGD    作者:angadgill    | 项目源码 | 文件源码
def _compute_mi_cc(x, y, n_neighbors):
    """Compute mutual information between two continuous variables.

    Parameters
    ----------
    x, y : ndarray, shape (n_samples,)
        Samples of two continuous random variables, must have an identical
        shape.

    n_neighbors : int
        Number of nearest neighbors to search for each point, see [1]_.

    Returns
    -------
    mi : float
        Estimated mutual information. If it turned out to be negative it is
        replace by 0.

    Notes
    -----
    True mutual information can't be negative. If its estimate by a numerical
    method is negative, it means (providing the method is adequate) that the
    mutual information is close to 0 and replacing it by 0 is a reasonable
    strategy.

    References
    ----------
    .. [1] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
           information". Phys. Rev. E 69, 2004.
    """
    n_samples = x.size

    x = x.reshape((-1, 1))
    y = y.reshape((-1, 1))
    xy = np.hstack((x, y))

    # Here we rely on NearestNeighbors to select the fastest algorithm.
    nn = NearestNeighbors(metric='chebyshev', n_neighbors=n_neighbors)

    nn.fit(xy)
    radius = nn.kneighbors()[0]
    radius = np.nextafter(radius[:, -1], 0)

    # Algorithm is selected explicitly to allow passing an array as radius
    # later (not all algorithms support this).
    nn.set_params(algorithm='kd_tree')

    nn.fit(x)
    ind = nn.radius_neighbors(radius=radius, return_distance=False)
    nx = np.array([i.size for i in ind])

    nn.fit(y)
    ind = nn.radius_neighbors(radius=radius, return_distance=False)
    ny = np.array([i.size for i in ind])

    mi = (digamma(n_samples) + digamma(n_neighbors) -
          np.mean(digamma(nx + 1)) - np.mean(digamma(ny + 1)))

    return max(0, mi)