Python numpy.ushort方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ushort [as 別名]
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ushort [as 別名]
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:  # pragma: no cover
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ushort [as 別名]
def _all_safe(self, features: np.ndarray, times: np.array,
                  censoring: np.array):
        if not set(np.unique(censoring)).issubset({0, 1}):
            raise ValueError('``censoring`` must only have values in {0, 1}')
        # All times must be positive
        if not np.all(times >= 0):
            raise ValueError('``times`` array must contain only non-negative '
                             'entries')
        features = safe_array(features)
        times = safe_array(times)
        censoring = safe_array(censoring, np.ushort)
        return features, times, censoring 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ushort [as 別名]
def _set_data(self, features: np.ndarray, times: np.array,
                  censoring: np.array):  #

        if self.dtype is None:
            self.dtype = features.dtype
            if self.dtype != times.dtype:
                raise ValueError("Features and labels differ in data types")

        n_samples, n_features = features.shape
        if n_samples != times.shape[0]:
            raise ValueError(("Features has %i samples while times "
                              "have %i" % (n_samples, times.shape[0])))
        if n_samples != censoring.shape[0]:
            raise ValueError(("Features has %i samples while censoring "
                              "have %i" % (n_samples, censoring.shape[0])))

        features = safe_array(features, dtype=self.dtype)
        times = safe_array(times, dtype=self.dtype)
        censoring = safe_array(censoring, np.ushort)

        self._set("features", features)
        self._set("times", times)
        self._set("censoring", censoring)
        self._set("n_samples", n_samples)
        self._set("n_features", n_features)
        self._set(
            "_model", dtype_class_mapper[self.dtype](self.features, self.times,
                                                     self.censoring)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ushort [as 別名]
def _simulate(self):
        # The features matrix already exists, and is created by the
        # super class
        features = self.features
        n_samples, n_features = features.shape
        u = features.dot(self.coeffs)
        # Simulation of true times
        E = np.random.exponential(scale=1., size=n_samples)
        E *= np.exp(-u)
        scale = self.scale
        shape = self.shape
        if self.times_distribution == "weibull":
            T = 1. / scale * E ** (1. / shape)
        else:
            # There is not point in this test, but let's do it like that
            # since we're likely to implement other distributions
            T = 1. / scale * E ** (1. / shape)
        m = T.mean()
        # Simulation of the censoring
        c = self.censoring_factor
        C = np.random.exponential(scale=c * m, size=n_samples)
        # Observed time
        self._set("times", np.minimum(T, C).astype(self.dtype))
        # Censoring indicator: 1 if it is a time of failure, 0 if it's
        #   censoring. It is as int8 and not bool as we might need to
        #   construct a memory access on it later
        censoring = (T <= C).astype(np.ushort)
        self._set("censoring", censoring)
        return self.features, self.times, self.censoring 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ushort [as 別名]
def test_SimuCoxReg(self):
        """...Test simulation of a Cox Regression
        """
        # Simulate a Cox model with specific seed
        n_samples = 10
        n_features = 3
        idx = np.arange(n_features)
        # Parameters of the Cox simu
        coeffs = np.exp(-idx / 10.)
        coeffs[::2] *= -1

        seed = 123
        simu = SimuCoxReg(coeffs, n_samples=n_samples, seed=seed,
                          verbose=False)
        features_, times_, censoring_ = simu.simulate()

        times = np.array([
            1.5022119, 5.93102441, 6.82837051, 0.50940341, 0.14859682,
            30.22922996, 3.54945974, 0.8671229, 1.4228358, 0.11483298
        ])

        censoring = np.array([1, 0, 1, 1, 1, 1, 1, 1, 0, 1], dtype=np.ushort)

        features = np.array([[1.4912667, 0.80881799, 0.26977298], [
            1.23227551, 0.50697013, 1.9409132
        ], [1.8891494, 1.49834791,
            2.41445794], [0.19431319, 0.80245126, 1.02577552], [
                                 -1.61687582, -1.08411865, -0.83438387
                             ], [2.30419894, -0.68987056,
                                 -0.39750262],
                             [-0.28826405, -1.23635074, -0.76124386], [
                                 -1.32869473, -1.8752391, -0.182537
                             ], [0.79464218, 0.65055633, 1.57572506],
                             [0.71524202, 1.66759831, 0.88679047]])

        np.testing.assert_almost_equal(features, features_)
        np.testing.assert_almost_equal(times, times_)
        np.testing.assert_almost_equal(censoring, censoring_) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ushort [as 別名]
def test_numpy(self):
        """NumPy objects get serialized to readable JSON."""
        l = [
            np.float32(12.5),
            np.float64(2.0),
            np.float16(0.5),
            np.bool(True),
            np.bool(False),
            np.bool_(True),
            np.unicode_("hello"),
            np.byte(12),
            np.short(12),
            np.intc(-13),
            np.int_(0),
            np.longlong(100),
            np.intp(7),
            np.ubyte(12),
            np.ushort(12),
            np.uintc(13),
            np.ulonglong(100),
            np.uintp(7),
            np.int8(1),
            np.int16(3),
            np.int32(4),
            np.int64(5),
            np.uint8(1),
            np.uint16(3),
            np.uint32(4),
            np.uint64(5),
        ]
        l2 = [l, np.array([1, 2, 3])]
        roundtripped = loads(dumps(l2, cls=EliotJSONEncoder))
        self.assertEqual([l, [1, 2, 3]], roundtripped) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ushort [as 別名]
def write_mhd_file(filename: PathLike, data: np.ndarray, **meta_dict):
    """
    Write a meta file and the raw file.
    The byte order of the raw file will always be in the byte order of the system. 

    :param filename: file to write
    :param meta_dict: dictionary of meta data in MetaImage format
    """
    assert filename[-4:] == '.mhd' 
    meta_dict['ObjectType'] = 'Image'
    meta_dict['BinaryData'] = 'True'
    meta_dict['BinaryDataByteOrderMSB'] = 'False' if sys.byteorder == 'little' else 'True'
    if data.dtype == np.float32:
        meta_dict['ElementType'] = 'MET_FLOAT'
    elif data.dtype == np.double or data.dtype == np.float64:
        meta_dict['ElementType'] = 'MET_DOUBLE'
    elif data.dtype == np.byte:
        meta_dict['ElementType'] = 'MET_CHAR'
    elif data.dtype == np.uint8 or data.dtype == np.ubyte:
        meta_dict['ElementType'] = 'MET_UCHAR'
    elif data.dtype == np.short or data.dtype == np.int16:
        meta_dict['ElementType'] = 'MET_SHORT'
    elif data.dtype == np.ushort or data.dtype == np.uint16:
        meta_dict['ElementType'] = 'MET_USHORT'
    elif data.dtype == np.int32:
        meta_dict['ElementType'] = 'MET_INT'
    elif data.dtype == np.uint32:
        meta_dict['ElementType'] = 'MET_UINT'
    else:
        raise NotImplementedError("ElementType " + str(data.dtype) + " not implemented.")
    dsize = list(data.shape)
    if 'ElementNumberOfChannels' in meta_dict.keys():
        element_channels = int(meta_dict['ElementNumberOfChannels'])
        assert(dsize[-1] == element_channels)
        dsize = dsize[:-1]
    else:
        element_channels = 1
    dsize.reverse()
    meta_dict['NDims'] = str(len(dsize))
    meta_dict['DimSize'] = dsize
    meta_dict['ElementDataFile'] = str(Path(filename).name).replace('.mhd', '.raw')
    print(str(Path(filename).name).replace('.mhd', '.raw'))

    # Tags that need conversion of list to string
    tags = ['ElementSpacing', 'Offset', 'DimSize', 'CenterOfRotation', 'TransformMatrix']
    for tag in tags:
        if tag in meta_dict.keys() and not isinstance(meta_dict[tag], str):
            meta_dict[tag] = ' '.join([str(i) for i in meta_dict[tag]])
    write_meta_header(filename, meta_dict)

    # Compute absolute path to write to
    pwd = Path(filename).parents[0].resolve()
    data_file = Path(meta_dict['ElementDataFile'])
    if not data_file.is_absolute():
        data_file = pwd / data_file

    # Dump raw data
    data = data.reshape(dsize[0], -1, element_channels)
    with open(data_file, 'wb') as f:
        data.tofile(f) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ushort [as 別名]
def test_SimuCoxRegWithCutPoints(self):
        """...Test simulation of a Cox Regression with cut-points
        """
        # Simulate a Cox model with cut-points with specific seed
        n_samples = 10
        n_features = 3
        n_cut_points = 2
        cov_corr = .5
        sparsity = .2

        seed = 123
        simu = SimuCoxRegWithCutPoints(n_samples=n_samples,
                                       n_features=n_features,
                                       seed=seed, verbose=False,
                                       n_cut_points=n_cut_points,
                                       shape=2, scale=.1, cov_corr=cov_corr,
                                       sparsity=sparsity)
        features_, times_, censoring_, cut_points_, coeffs_binarized_, S_ = simu.simulate()

        times = np.array([6.12215425, 6.74403919, 5.2148425, 5.42903238,
                          2.42953933, 9.50705158, 18.49545933, 19.7929349,
                          0.39267278, 1.24799812])

        censoring = np.array([1, 0, 0, 1, 0, 1, 1, 1, 0, 1], dtype=np.ushort)

        features = np.array([[1.4912667, 0.80881799, 0.26977298],
                             [1.23227551, 0.50697013, 1.9409132],
                             [1.8891494, 1.49834791, 2.41445794],
                             [0.19431319, 0.80245126, 1.02577552],
                             [-1.61687582, -1.08411865, -0.83438387],
                             [2.30419894, -0.68987056, -0.39750262],
                             [-0.28826405, -1.23635074, -0.76124386],
                             [-1.32869473, -1.8752391, -0.182537],
                             [0.79464218, 0.65055633, 1.57572506],
                             [0.71524202, 1.66759831, 0.88679047]])

        cut_points = {'0': np.array([-np.inf, -0.28826405, 0.79464218, np.inf]),
                      '1': np.array([-np.inf, -1.23635074, 0.50697013, np.inf]),
                      '2': np.array([-np.inf, -0.182537, 0.88679047, np.inf])}

        coeffs_binarized = np.array([-1.26789642, 1.31105319, -0.04315676, 0.,
                                     0., 0., 0.01839684, 0.4075832,
                                     -0.42598004])

        S = np.array([1])

        np.testing.assert_almost_equal(features, features_)
        np.testing.assert_almost_equal(times, times_)
        np.testing.assert_almost_equal(censoring, censoring_)
        np.testing.assert_almost_equal(cut_points_['0'], cut_points['0'])
        np.testing.assert_almost_equal(cut_points_['1'], cut_points['1'])
        np.testing.assert_almost_equal(cut_points_['2'], cut_points['2'])
        np.testing.assert_almost_equal(coeffs_binarized, coeffs_binarized_)
        np.testing.assert_almost_equal(S, S_)