Python numpy.ndarry() Examples

The following are 9 code examples for showing how to use numpy.ndarry(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: garage   Author: rlworkgroup   File:    License: MIT License 5 votes vote down vote up
def get_actions_given_tasks(self, observations, task_ids):
        """Sample a batch of actions given observations and task ids.

            observations (np.ndarray): Observations from the environment, with
                shape :math:`(T, O)`. T is the number of environment steps,
                O is the dimension of observation.
            task_ids (np.ndarry): One-hot task ids, with shape :math:`(T, N)`.
                T is the number of environment steps, N is the number of tasks.

            np.ndarray: Actions sampled from the policy,
                with shape :math:`(T, A)`. T is the number of environment
                steps, A is the dimension of action.
            dict: Action distribution information, , with keys:
                - mean (numpy.ndarray): Mean of the distribution,
                    with shape :math:`(T, A)`. T is the number of
                    environment steps. A is the dimension of action.
                - log_std (numpy.ndarray): Log standard deviation of the
                    distribution, with shape :math:`(T, A)`. T is the number of
                    environment steps. A is the dimension of action.

        flat_obses = self.observation_space.flatten_n(observations)
        flat_obses = np.expand_dims(flat_obses, 1)
        task_ids = np.expand_dims(task_ids, 1)

        samples, means, log_stds = self._f_dist_obs_task(flat_obses, task_ids)
        samples = self.action_space.unflatten_n(np.squeeze(samples, 1))
        means = self.action_space.unflatten_n(np.squeeze(means, 1))
        log_stds = self.action_space.unflatten_n(np.squeeze(log_stds, 1))
        return samples, dict(mean=means, log_std=log_stds) 
Example 2
Project: abcpy   Author: eth-cscs   File:    License: BSD 3-Clause Clear License 5 votes vote down vote up
def _polynomial_expansion(self, summary_statistics):
        """Helper function that does the polynomial expansion and includes cross-product
        terms of summary_statistics, already calculated summary statistics.

        summary_statistics: numpy.ndarray
            nxp matrix where n is number of data points in the datasets data set and p number os
            summary statistics calculated.
            nx(p+degree*p+cross*nchoosek(p,2)) matrix where for each of the n pointss with
            p statistics, degree*p polynomial expansion term and cross*nchoosek(p,2) many
            cross-product terms are calculated.


        # Check summary_statistics is a np.ndarry
        if not isinstance(summary_statistics, np.ndarray):
            raise TypeError('Summary statistics is not of allowed types')
        # Include the polynomial expansion
        result = summary_statistics
        for ind in range(2, + 1):
            result = np.column_stack((result, np.power(summary_statistics, ind)))

        # Include the cross-product term
        if self.cross == True and summary_statistics.shape[1] > 1:
            # Convert to a matrix
            for ind1 in range(0, summary_statistics.shape[1]):
                for ind2 in range(ind1 + 1, summary_statistics.shape[1]):
                    result = np.column_stack((result, summary_statistics[:, ind1] * summary_statistics[:, ind2]))
        return result 
Example 3
Project: Printed-Text-recognition-and-conversion   Author: ishita27   File:    License: MIT License 5 votes vote down vote up
def load_data_wrapper():
	"""Return a tuple containing ``(training_data, validation_data,
	test_data)``. Based on ``load_data``, but the format is more
	convenient for use in our implementation of neural networks.

	In particular, ``training_data`` is a list containing 50,000
	2-tuples ``(x, y)``.  ``x`` is a 784-dimensional numpy.ndarray
	containing the input image.  ``y`` is a 10-dimensional
	numpy.ndarray representing the unit vector corresponding to the
	correct digit for ``x``.

	``validation_data`` and ``test_data`` are lists containing 10,000
	2-tuples ``(x, y)``.  In each case, ``x`` is a 784-dimensional
	numpy.ndarry containing the input image, and ``y`` is the
	corresponding classification, i.e., the digit values (integers)
	corresponding to ``x``.

	Obviously, this means we're using slightly different formats for
	the training data and the validation / test data.  These formats
	turn out to be the most convenient for use in our neural network
	tr_d, va_d, te_d = load_data()
	print("Shape of training data",tr_d.shape)

	training_inputs = [np.reshape(x, (1024, 1)) for x in tr_d[0]]
	training_results = [vectorized_result(y) for y in tr_d[1]]
	training_data = zip(training_inputs, training_results)

	validation_inputs = [np.reshape(x, (1024, 1)) for x in va_d[0]]
	validation_data = zip(validation_inputs, va_d[1])

	test_inputs = [np.reshape(x, (1024, 1)) for x in te_d[0]]
	test_data = zip(test_inputs, te_d[1])

	return (training_data, validation_data, test_data) 
Example 4
Project: Image-stitcher   Author: zhaobenx   File:    License: MIT License 5 votes vote down vote up
def k_means(points: np.ndarray):

        points (np.ndarray): 需分类数据

        Tuple[int, np.ndarry]: k值以及标签数组

    # Define criteria = ( type, max_iter = 10 , epsilon = 1.0 )
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

    # Set flags (Just to avoid line break in the code)
    length = []
    max_k = min(10, points.shape[0])
    for k in range(2, max_k + 1):
        avg = 0
        for i in range(5):
            compactness, _, _ = cv2.kmeans(
                points, k, None, criteria, 10, flags)
            avg += compactness
        avg /= 5

    peek_pos = find_peek(length)
    k = peek_pos + 2
    # print(k)
    return k, cv2.kmeans(points, k, None, criteria, 10, flags)[1]  # labels 
Example 5
Project: pymer4   Author: ejolly   File:    License: MIT License 5 votes vote down vote up
def con2R(arr):
    Convert user desired contrasts to R-flavored contrast matrix that can be passed directly to lm(). Reference:

        arr (np.ndarry): 2d numpy array arranged as contrasts X factor levels

        np.ndarray: 2d contrast matrix as expected by R's contrasts() function

    intercept = np.repeat(1.0 / arr.shape[1], arr.shape[1])
    mat = np.vstack([intercept, arr])
    inv = np.linalg.inv(mat)[:, 1:]
    return inv 
Example 6
Project: pymer4   Author: ejolly   File:    License: MIT License 5 votes vote down vote up
def R2con(arr):
    Convert R-flavored contrast matrix to intepretable contrasts as would be specified by user. Reference:

        arr (np.ndarry): 2d contrast matrix output from R's contrasts() function.

        np.ndarray: 2d array organized as contrasts X factor levels

    intercept = np.ones((arr.shape[0], 1))
    mat = np.column_stack([intercept, arr])
    inv = np.linalg.inv(mat)
    return inv 
Example 7
Project: gempy   Author: cgre-aachen   File:    License: GNU Lesser General Public License v3.0 5 votes vote down vote up
def set_regular_grid(self, extent, resolution):
        Set a regular grid into the values parameters for further computations
             extent (list, np.ndarry):  [x_min, x_max, y_min, y_max, z_min, z_max]
            resolution (list, np.ndarray): [nx, ny, nz]

        self.extent = np.asarray(extent, dtype='float64')
        self.resolution = np.asarray(resolution)
        self.values = self.create_regular_grid_3d(extent, resolution)
        self.length = self.values.shape[0]
        self.dx, self.dy, = self.get_dx_dy_dz()
        return self.values 
Example 8
Project: scikit_tt   Author: PGelss   File:    License: GNU Lesser General Public License v3.0 4 votes vote down vote up
def __slim_tcr_decomposition(super_core, threshold):
    Two-cell reaction decomposition.

    Decompose a super-core representing the interactions between two cells.

    super_core : np.ndarray
        tensor with order 4
    threshold : float
            threshold for reduced SVD decompositions

    core_left : np.ndarray
        TT core for first cell
    core_right : np.ndarry
        TT core for second cell
    rank : int
        TT rank

    # number of states
    dimension_1 = super_core.shape[0]
    dimension_2 = super_core.shape[2]

    # apply SVD in order to split the super-core
    [u, s, v] = linalg.svd(super_core.reshape(dimension_1 ** 2, dimension_2 ** 2),
                           full_matrices=False, overwrite_a=True, check_finite=False, lapack_driver='gesvd')

    # rank reduction
    if threshold != 0:
        indices = np.where(s / s[0] > threshold)[0]
        u = u[:, indices]
        s = s[indices]
        v = v[indices, :]

    # set quantities for decomposition
    rank = u.shape[1]
    core_left = (, dimension_1, rank)
    core_right = v.reshape(rank, dimension_2, dimension_2)
    return core_left, core_right, rank 
Example 9
Project: orbkit   Author: orbkit   File:    License: GNU Lesser General Public License v3.0 4 votes vote down vote up
def ao2mo(mat, coeffs, MOrange=None, MOrangei=None, MOrangej=None, MOrangek=None, MOrangel=None):
  '''Transforms array of one- or two-electron integrals from AO to MO basis.


  mat : 2 or 4 dimensional numpy.ndarray
    integrals to be transformed
  coeffs : 2 dimensional numpy.ndarry
    MO coefficients
  MOrangei, MOrangej, MOrangek, MOrangel : list|range object|None
    Only transform selected MOs for indices i, j, k and l respectively.
  MOrange: list or range object or None
    Set same range for all indices.



  assert len(mat.shape) in (2, 4), "'mat' musst be of size 2 or 4."

  if MOrange is not None:
    MOrangei = MOrange
    MOrangej = MOrange
    MOrangek = MOrange
    MOrangel = MOrange
  if MOrangei is None:
    MOrangei = range(coeffs.shape[0])
  if MOrangej is None:
    MOrangej = range(coeffs.shape[0])
  if MOrangek is None:
    MOrangek = range(coeffs.shape[0])
  if MOrangel is None:
    MOrangel = range(coeffs.shape[0])

  # discard zero columns in MO coeffs
  if len(mat.shape) == 2:
    MOs = list(set(chain(MOrangei, MOrangej)))
  elif len(mat.shape) == 4:
    MOs = list(set(chain(MOrangei, MOrangej, MOrangek, MOrangel)))
  AOs = numpy.where(~numpy.isclose(coeffs[MOs,:], 1e-15).all(axis=0))[0]
  coeffs = coeffs[:,AOs]

  if len(mat.shape) == 2:
    # 1-electron integrals
    mat = mat[numpy.ix_(AOs, AOs)]
    return[MOrangei,:],, coeffs[MOrangej,:].T))
  elif len(mat.shape) == 4:
    # 2-electron integrals
    mat = mat[numpy.ix_(AOs, AOs, AOs, AOs)]
    mat = numpy.tensordot(mat, coeffs[MOrangei,:], axes=(0, 1))
    mat = numpy.tensordot(mat, coeffs[MOrangej,:], axes=(0, 1))
    mat = numpy.tensordot(mat, coeffs[MOrangek,:], axes=(0, 1))
    mat = numpy.tensordot(mat, coeffs[MOrangel,:], axes=(0, 1))
    return mat