python类fmax()的实例源码

def imax(arrays, axis, ignore_nan = False):
    """ 
    Maximum of a stream of arrays along an axis.

    Parameters
    ----------
    arrays : iterable
        Arrays to be reduced.
    axis : int or None, optional
        Axis along which the maximum is found. The default
        is to find the maximum along the 'stream axis', as if all arrays in ``array``
        were stacked along a new dimension. If ``axis = None``, arrays in ``arrays`` are flattened
        before reduction.
    ignore_nan : bool, optional
        If True, NaNs are ignored. Default is propagation of NaNs.

    Yields
    ------
    online_max : ndarray
        Cumulative maximum.
    """
    ufunc = np.fmax if ignore_nan else np.maximum
    yield from ireduce_ufunc(arrays, ufunc, axis)

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmax.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 9)
            assert_equal(func(tmp2), 9)

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)

def fea_plot(xg_model, feature, label, type = 'weight', max_num_features = None):
    fig, AX = plt.subplots(nrows=1, ncols=2)
    xgb.plot_importance(xg_model, xlabel=type, importance_type='weight', ax=AX[0], max_num_features=max_num_features)

    fscore = xg_model.get_score(importance_type=type)
    fscore = sorted(fscore.items(), key=itemgetter(1), reverse=True) # sort scores
    fea_index = get_fea_index(fscore, max_num_features)
    feature = feature[:, fea_index]
    dimension = len(fea_index)
    X = range(1, dimension+1)
    Yp = np.mean(feature[np.where(label==1)[0]], axis=0)
    Yn = np.mean(feature[np.where(label!=1)[0]], axis=0)
    for i in range(0, dimension):
        param = np.fmax(Yp[i], Yn[i])
        Yp[i] /= param
        Yn[i] /= param
    p1 = AX[1].bar(X, +Yp, facecolor='#ff9999', edgecolor='white')
    p2 = AX[1].bar(X, -Yn, facecolor='#9999ff', edgecolor='white')
    AX[1].legend((p1,p2), ('Malware', 'Normal'))
    AX[1].set_title('Comparison of selected features by their means')
    AX[1].set_xlabel('Feature Index')
    AX[1].set_ylabel('Mean Value')
    AX[1].set_ylim(-1.1, 1.1)
    plt.xticks(X, fea_index+1, rotation=80)
    plt.suptitle('Feature Selection results')

def test_obj_value_points_correctly_class_far_from_hyperplane(self):
        bias = 0.0

        w = np.array([-1, 1, bias])
        l = self.svm.l2reg
        X = np.array([[5, 0.3], [1, -0.8], [1, 6], [-0.6, 3]])
        Y = np.array([-1, -1, 1, 1])

        # compute loss for all X -> 1-yi*(xi*w+b)
        out = np.fmax(0, 1 - Y * (X.dot(w[0:-1]) + w[-1]))

        expectedObj = 1.0

        result, _ = self.svm._obj_func(w, X, Y, out)

        self.assertAlmostEqual(expectedObj, result)

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmax.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 9)
            assert_equal(func(tmp2), 9)

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmax.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 9)
            assert_equal(func(tmp2), 9)

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmax.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 9)
            assert_equal(func(tmp2), 9)

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmax.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 9)
            assert_equal(func(tmp2), 9)

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmax.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 9)
            assert_equal(func(tmp2), 9)

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)

def variability_prob(ndvi, ndsi, whiteness):
    """Use the probability of the spectral variability
    to identify clouds over land.

    Equation 15 (Zhu and Woodcock, 2012)

    Parameters
    ----------
    ndvi: ndarray
    ndsi: ndarray
    whiteness: ndarray

    Output
    ------
    ndarray :
        probability of cloud over land based on variability
    """
    ndi_max = np.fmax(np.absolute(ndvi), np.absolute(ndsi))
    f_max =  1.0 - np.fmax(ndi_max, whiteness)
    return f_max


# Eq 16, land_cloud_prob
# lCloud_Prob = lTemperature_Prob x Variability_Prob

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmax.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 9)
            assert_equal(func(tmp2), 9)

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)

def test_reduce_complex(self):
        assert_equal(np.fmax.reduce([1, 2j]), 1)
        assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)

def test_float_nans(self):
        nan = np.nan
        arg1 = np.array([0,   nan, nan])
        arg2 = np.array([nan, 0,   nan])
        out = np.array([0,   0,   nan])
        assert_equal(np.fmax(arg1, arg2), out)

def test_complex_nans(self):
        nan = np.nan
        for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
            arg1 = np.array([0, cnan, cnan], dtype=np.complex)
            arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
            out = np.array([0,    0, nan], dtype=np.complex)
            assert_equal(np.fmax(arg1, arg2), out)

def _obj_func(self, w, X, Y, out):
        """
        Computes primal value end gradient
        Parameters
        ----------
        w : {array-like} - hyperplane normal vector
        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
            Training vector, where n_samples in the number of samples and
            n_features is the number of features.
        Y : array-like, shape = [n_samples]
            Target vector relative to X
        out: loss function values
        Returns
        -------
        (obj,grad) : tuple, obj - function value, grad - gradient

        """

        l2reg = self.l2reg

        # we remember bias, to recover it after gradient computation
        bias = w[-1]
        # set bias to zero, don't penalize b
        w[-1] = 0

        max_out = np.fmax(0, out)
        obj = np.sum(max_out ** 2) / 2 + l2reg * w.dot(w) / 2

        grad = l2reg * w - np.append([np.dot(max_out * Y, X)], [np.sum(max_out * Y)])

        w[-1] = bias

        return (obj, grad)

def test_obj_value_points_correctly_class_close_to_hyperplane(self):
        bias = 0.0
        w = np.array([-1, 1, bias])
        l = self.svm.l2reg
        X = np.array([[0.5, 0.3], [1, 0.8], [1, 1.4], [0.6, 0.9]])
        Y = np.array([-1, -1, 1, 1])

        # compute loss for all X -> 1-yi*(xi*w+b)
        out = np.fmax(0, 1 - Y * (X.dot(w[0:-1]) + w[-1]))

        expectedObj = 2.0650000000000004

        result, _ = self.svm._obj_func(w, X, Y, out)

        self.assertAlmostEqual(expectedObj, result)

def test_obj_grad_points(self):
        bias = 0.0
        w = np.array([-1, 1, bias])
        l = self.svm.l2reg
        X = np.array([[0.5, 0.3], [1, 0.8], [1, 1.4], [0.6, 0.9]])
        Y = np.array([-1, -1, 1, 1])

        # compute loss for all X -> 1-yi*(xi*w+b)
        out = np.fmax(0, 1 - Y * (X.dot(w[0:-1]) + w[-1]))

        expected = np.array([-0.82, 0.41, 0.3])

        (obj, grad) = self.svm._obj_func(w, X, Y, out)

        np.testing.assert_array_almost_equal(expected, grad)

def _scale_cosine_similarity(x, metric='cosine', inverse=False):
    """ Given a cosine similarity on L2 normalized data,
    appriximately convert it to Jaccard similarity, and/or
    normalize it to the [0, 1] interval

    Parameters
    ----------
    x : {float, ndarray}
      the cosine similarity value
    metric : str
      the conversion to apply one of ['cosine', 'jaccard']
    inverse : bool
      perform the inverse de-normalization operation
    """
    valid_metrics = ['cosine', 'jaccard', 'cosine_norm', 'jaccard_norm',
                     'cosine-positive']
    if metric not in valid_metrics:
        raise ValueError('metric {} not supported, must be in {}'
                         .format(metric, valid_metrics))
    if metric == 'cosine':
        return x
    elif metric == 'cosine-positive':
        if isinstance(x, (int, float)):
            return max(x, 0.0)
        else:
            return np.fmax(x, 0.0)

    if metric.startswith('jaccard'):
        if not inverse:
            x = cosine2jaccard_similarity(x)
        else:
            x = jaccard2cosine_similarity(x)

    if metric.endswith('norm'):
        x = _normalize_similarity(x, metric=metric.split('_')[0],
                                  inverse=inverse)

    return x

def test_reduce_complex(self):
        assert_equal(np.fmax.reduce([1, 2j]), 1)
        assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)

def test_float_nans(self):
        nan = np.nan
        arg1 = np.array([0,   nan, nan])
        arg2 = np.array([nan, 0,   nan])
        out = np.array([0,   0,   nan])
        assert_equal(np.fmax(arg1, arg2), out)

def test_complex_nans(self):
        nan = np.nan
        for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
            arg1 = np.array([0, cnan, cnan], dtype=np.complex)
            arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
            out = np.array([0,    0, nan], dtype=np.complex)
            assert_equal(np.fmax(arg1, arg2), out)

def forward_cpu(self, inputs):
        x, = inputs
        # y = log(1 + exp(beta * x)) / beta
        bx = self.beta * x
        y = (numpy.fmax(bx, 0) +
             numpy.log1p(numpy.exp(-numpy.fabs(bx)))) * self.beta_inv
        return utils.force_array(y.astype(x.dtype)),

def test_reduce_complex(self):
        assert_equal(np.fmax.reduce([1, 2j]), 1)
        assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)