Python numpy.uint32方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def residual_resample(weights):
    n = len(weights)
    indices = np.zeros(n, np.uint32)
    # take int(N*w) copies of each weight
    num_copies = (n * weights).astype(np.uint32)
    k = 0
    for i in range(n):
        for _ in range(num_copies[i]):  # make n copies
            indices[k] = i
            k += 1
    # use multinormial resample on the residual to fill up the rest.
    residual = weights - num_copies  # get fractional part
    residual /= np.sum(residual)
    cumsum = np.cumsum(residual)
    cumsum[-1] = 1
    indices[k:n] = np.searchsorted(cumsum, np.random.uniform(0, 1, n - k))
    return indices 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def create_indices(positions, weights):
    n = len(weights)
    indices = np.zeros(n, np.uint32)
    cumsum = np.cumsum(weights)
    i, j = 0, 0
    while i < n:
        if positions[i] < cumsum[j]:
            indices[i] = j
            i += 1
        else:
            j += 1

    return indices


### end rlabbe's resampling functions 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def test_can_cast():
    tests = ((np.float32, np.float32, True, True, True),
             (np.float64, np.float32, True, True, True),
             (np.complex128, np.float32, False, False, False),
             (np.float32, np.complex128, True, True, True),
             (np.float32, np.uint8, False, True, True),
             (np.uint32, np.complex128, True, True, True),
             (np.int64, np.float32, True, True, True),
             (np.complex128, np.int16, False, False, False),
             (np.float32, np.int16, False, True, True),
             (np.uint8, np.int16, True, True, True),
             (np.uint16, np.int16, False, True, True),
             (np.int16, np.uint16, False, False, True),
             (np.int8, np.uint16, False, False, True),
             (np.uint16, np.uint8, False, True, True),
             )
    for intype, outtype, def_res, scale_res, all_res in tests:
        assert_equal(def_res, can_cast(intype, outtype))
        assert_equal(scale_res, can_cast(intype, outtype, False, True))
        assert_equal(all_res, can_cast(intype, outtype, True, True)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def stream2words(stream, track=None):
    """Convert a stream of integers to uint32 header words.

    Parameters
    ----------
    stream : `~numpy.array` of int
        For each int, every bit corresponds to a particular track.
    track : int, array, or None, optional
        The track to extract.  If `None` (default), extract all tracks that
        the type of int in the stream can hold.
    """
    if track is None:
        track = np.arange(stream.dtype.itemsize * 8, dtype=stream.dtype)

    track_sel = ((stream.reshape(-1, 32, 1) >> track) & 1).astype(np.uint32)
    track_sel <<= np.arange(31, -1, -1, dtype=np.uint32).reshape(-1, 1)
    words = np.bitwise_or.reduce(track_sel, axis=1)
    return words.squeeze() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def words2stream(words):
    """Convert a set of uint32 header words to a stream of integers.

    Parameters
    ----------
    words : `~numpy.array` of uint32

    Returns
    -------
    stream : `~numpy.array` of int
        For each int, every bit corresponds to a particular track.
    """
    ntrack = words.shape[1]
    dtype = MARK4_DTYPES[ntrack]
    nbits = words.dtype.itemsize * 8
    bit = np.arange(nbits - 1, -1, -1, dtype=words.dtype).reshape(-1, 1)

    bit_sel = ((words[:, np.newaxis, :] >> bit) & 1).astype(dtype[1:])
    bit_sel <<= np.arange(ntrack, dtype=dtype[1:])
    words = np.empty(bit_sel.shape[:2], dtype)
    words = np.bitwise_or.reduce(bit_sel, axis=2, out=words)
    return words.ravel() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def __yield_buffer(self, path, feat_mapper, defaults, buffer_size=int(1e5)):
        item_idx = 0
        buffer = list()
        with open(path) as f:
            f.readline()
            pbar = tqdm(f, mininterval=1, smoothing=0.1)
            pbar.set_description('Create avazu dataset cache: setup lmdb')
            for line in pbar:
                values = line.rstrip('\n').split(',')
                if len(values) != self.NUM_FEATS + 2:
                    continue
                np_array = np.zeros(self.NUM_FEATS + 1, dtype=np.uint32)
                np_array[0] = int(values[1])
                for i in range(1, self.NUM_FEATS + 1):
                    np_array[i] = feat_mapper[i].get(values[i+1], defaults[i])
                buffer.append((struct.pack('>I', item_idx), np_array.tobytes()))
                item_idx += 1
                if item_idx % buffer_size == 0:
                    yield buffer
                    buffer.clear()
            yield buffer 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def __yield_buffer(self, path, feat_mapper, defaults, buffer_size=int(1e5)):
        item_idx = 0
        buffer = list()
        with open(path) as f:
            pbar = tqdm(f, mininterval=1, smoothing=0.1)
            pbar.set_description('Create criteo dataset cache: setup lmdb')
            for line in pbar:
                values = line.rstrip('\n').split('\t')
                if len(values) != self.NUM_FEATS + 1:
                    continue
                np_array = np.zeros(self.NUM_FEATS + 1, dtype=np.uint32)
                np_array[0] = int(values[0])
                for i in range(1, self.NUM_INT_FEATS + 1):
                    np_array[i] = feat_mapper[i].get(convert_numeric_feature(values[i]), defaults[i])
                for i in range(self.NUM_INT_FEATS + 1, self.NUM_FEATS + 1):
                    np_array[i] = feat_mapper[i].get(values[i], defaults[i])
                buffer.append((struct.pack('>I', item_idx), np_array.tobytes()))
                item_idx += 1
                if item_idx % buffer_size == 0:
                    yield buffer
                    buffer.clear()
            yield buffer 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def _predict_spatial(self, earray, stim):
        """Predicts the brightness at specific times ``t``"""
        # This does the expansion of a compact stimulus and a list of
        # electrodes to activation values at X,Y grid locations:
        assert isinstance(earray, ElectrodeArray)
        assert isinstance(stim, Stimulus)
        return fast_axon_map(stim.data,
                             np.array([earray[e].x for e in stim.electrodes],
                                      dtype=np.float32),
                             np.array([earray[e].y for e in stim.electrodes],
                                      dtype=np.float32),
                             self.axon_contrib,
                             self.axon_idx_start.astype(np.uint32),
                             self.axon_idx_end.astype(np.uint32),
                             self.rho,
                             self.thresh_percept) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def _predict_temporal(self, stim, t_percept):
        """Predict the temporal response"""
        # Pass the stimulus as a 2D NumPy array to the fast Cython function:
        stim_data = stim.data.reshape((-1, len(stim.time)))
        # Calculate at which simulation time steps we need to output a percept.
        # This is basically t_percept/self.dt, but we need to beware of
        # floating point rounding errors! 29.999 will be rounded down to 29 by
        # np.uint32, so we need to np.round it first:
        idx_percept = np.uint32(np.round(t_percept / self.dt))
        if np.unique(idx_percept).size < t_percept.size:
            raise ValueError("All times 't_percept' must be distinct multiples "
                             "of `dt`=%.2e" % self.dt)
        # Cython returns a 2D (space x time) NumPy array:
        return temporal_fast(stim_data.astype(np.float32),
                             stim.time.astype(np.float32),
                             idx_percept,
                             self.dt, self.tau1, self.tau2, self.tau3,
                             self.eps, self.beta, self.thresh_percept) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def _predict_temporal(self, stim, t_percept):
        """Predict the temporal response"""
        # Pass the stimulus as a 2D NumPy array to the fast Cython function:
        stim_data = stim.data.reshape((-1, len(stim.time)))
        # Calculate at which simulation time steps we need to output a percept.
        # This is basically t_percept/self.dt, but we need to beware of
        # floating point rounding errors! 29.999 will be rounded down to 29 by
        # np.uint32, so we need to np.round it first:
        idx_percept = np.uint32(np.round(t_percept / self.dt))
        if np.unique(idx_percept).size < t_percept.size:
            raise ValueError("All times 't_percept' must be distinct multiples "
                             "of `dt`=%.2e" % self.dt)
        # Cython returns a 2D (space x time) NumPy array:
        return temporal_fast(stim_data.astype(np.float32),
                             stim.time.astype(np.float32),
                             idx_percept,
                             self.dt, self.tau1, self.tau2, self.tau3,
                             self.asymptote, self.shift, self.slope, self.eps,
                             self.thresh_percept) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def squeeze_bits(arr: numpy.ndarray) -> numpy.ndarray:
    """Return a copy of an integer numpy array with the minimum bitness."""
    assert arr.dtype.kind in ("i", "u")
    if arr.size == 0:
        return arr
    if arr.dtype.kind == "i":
        assert arr.min() >= 0
    mlbl = int(arr.max()).bit_length()
    if mlbl <= 8:
        dtype = numpy.uint8
    elif mlbl <= 16:
        dtype = numpy.uint16
    elif mlbl <= 32:
        dtype = numpy.uint32
    else:
        dtype = numpy.uint64
    return arr.astype(dtype) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def test_padded_union(self):
        dt = np.dtype(dict(
            names=['a', 'b'],
            offsets=[0, 0],
            formats=[np.uint16, np.uint32],
            itemsize=5,
        ))

        ct = np.ctypeslib.as_ctypes_type(dt)
        assert_(issubclass(ct, ctypes.Union))
        assert_equal(ctypes.sizeof(ct), dt.itemsize)
        assert_equal(ct._fields_, [
            ('a', ctypes.c_uint16),
            ('b', ctypes.c_uint32),
            ('', ctypes.c_char * 5),  # padding
        ]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
                      np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)
            if ctype in ['1', 'b']:
                assert_raises(ArithmeticError, np.prod, a)
                assert_raises(ArithmeticError, np.prod, a2, 1)
            else:
                assert_equal(a.prod(axis=0), 26400)
                assert_array_equal(a2.prod(axis=0),
                                   np.array([50, 36, 84, 180], ctype))
                assert_array_equal(a2.prod(axis=-1),
                                   np.array([24, 1890, 600], ctype)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def test_setdiff1d(self):
        a = np.array([6, 5, 4, 7, 1, 2, 7, 4])
        b = np.array([2, 4, 3, 3, 2, 1, 5])

        ec = np.array([6, 7])
        c = setdiff1d(a, b)
        assert_array_equal(c, ec)

        a = np.arange(21)
        b = np.arange(19)
        ec = np.array([19, 20])
        c = setdiff1d(a, b)
        assert_array_equal(c, ec)

        assert_array_equal([], setdiff1d([], []))
        a = np.array((), np.uint32)
        assert_equal(setdiff1d(a, []).dtype, np.uint32) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint32 [as 別名]
def test_union_with_struct_packed(self):
        class Struct(ctypes.Structure):
            _pack_ = 1
            _fields_ = [
                ('one', ctypes.c_uint8),
                ('two', ctypes.c_uint32)
            ]

        class Union(ctypes.Union):
            _fields_ = [
                ('a', ctypes.c_uint8),
                ('b', ctypes.c_uint16),
                ('c', ctypes.c_uint32),
                ('d', Struct),
            ]
        expected = np.dtype(dict(
            names=['a', 'b', 'c', 'd'],
            formats=['u1', np.uint16, np.uint32, [('one', 'u1'), ('two', np.uint32)]],
            offsets=[0, 0, 0, 0],
            itemsize=ctypes.sizeof(Union)
        ))
        self.check(Union, expected)