Python numpy.ones() Examples

The following are code examples for showing how to use numpy.ones(). They are extracted from open source Python projects. You can vote up the examples you like or vote down the ones you don't like. You can also save this page to your account.

Example 1
Project: pylspm   Author: lseman   File: adequacy.py    (MIT License) View Source Project 7 votes vote down vote up
def KMO(data):

    cor_ = pd.DataFrame.corr(data)
    invCor = np.linalg.inv(cor_)
    rows = cor_.shape[0]
    cols = cor_.shape[1]
    A = np.ones((rows, cols))

    for i in range(rows):
        for j in range(i, cols):
            A[i, j] = - (invCor[i, j]) / (np.sqrt(invCor[i, i] * invCor[j, j]))
            A[j, i] = A[i, j]

    num = np.sum(np.sum((cor_)**2)) - np.sum(np.sum(np.diag(cor_**2)))
    den = num + (np.sum(np.sum(A**2)) - np.sum(np.sum(np.diag(A**2))))
    kmo = num / den

    return kmo 
Example 2
Project: segmentation_DLMI   Author: imatge-upc   File: data_loader_test.py    (license) View Source Project 7 votes vote down vote up
def load_ROI_mask(self):

        proxy = nib.load(self.FLAIR_FILE)
        image_array = np.asarray(proxy.dataobj)

        mask = np.ones_like(image_array)
        mask[np.where(image_array < 90)] = 0

        # img = nib.Nifti1Image(mask, proxy.affine)
        # nib.save(img, join(modalities_path,'mask.nii.gz'))

        struct_element_size = (20, 20, 20)
        mask_augmented = np.pad(mask, [(21, 21), (21, 21), (21, 21)], 'constant', constant_values=(0, 0))
        mask_augmented = binary_closing(mask_augmented, structure=np.ones(struct_element_size, dtype=bool)).astype(
            np.int)

        return mask_augmented[21:-21, 21:-21, 21:-21].astype('bool') 
Example 3
Project: pyfds   Author: emtpb   File: gfx.py    (BSD 3-Clause "New" or "Revised" License) View Source Project 6 votes vote down vote up
def plot_region(self, region):
        """Shows the given region in the field plot.

        Args:
            region: Region to be plotted.
        """

        if type(region) == reg.PointRegion:
            self.axes.plot(np.ones(2) * region.point_coordinates / self._x_axis_factor,
                           np.array([-1, 1]) * self.scale, color='black')
        elif type(region) == reg.LineRegion:
            self.axes.plot(np.ones(2) * region.line_coordinates[0] / self._x_axis_factor,
                           np.array([-1, 1]) * self.scale, color='black')
            self.axes.plot(np.ones(2) * region.line_coordinates[1] / self._x_axis_factor,
                           np.array([-1, 1]) * self.scale, color='black')
        else:
            raise TypeError('Unknown type in region list: {}'.format(type(region))) 
Example 4
Project: treecat   Author: posterior   File: serving.py    (Apache License 2.0) View Source Project 6 votes vote down vote up
def observed_perplexity(self, counts):
        """Compute perplexity = exp(entropy) of observed variables.

        Perplexity is an information theoretic measure of the number of
        clusters or latent classes. Perplexity is a real number in the range
        [1, M], where M is model_num_clusters.

        Args:
            counts: A [V]-shaped array of multinomial counts.

        Returns:
            A [V]-shaped numpy array of perplexity.
        """
        V, E, M, R = self._VEMR
        if counts is not None:
            counts = np.ones(V, dtype=np.int8)
        assert counts.shape == (V, )
        assert counts.dtype == np.int8
        assert np.all(counts > 0)
        observed_entropy = np.empty(V, dtype=np.float32)
        for v in range(V):
            beg, end = self._ragged_index[v:v + 2]
            probs = np.dot(self._feat_cond[beg:end, :], self._vert_probs[v, :])
            observed_entropy[v] = multinomial_entropy(probs, counts[v])
        return np.exp(observed_entropy) 
Example 5
Project: squeezeDet-hand   Author: fyhtea   File: util.py    (BSD 2-Clause "Simplified" License) View Source Project 6 votes vote down vote up
def sparse_to_dense(sp_indices, output_shape, values, default_value=0):
  """Build a dense matrix from sparse representations.

  Args:
    sp_indices: A [0-2]-D array that contains the index to place values.
    shape: shape of the dense matrix.
    values: A {0,1}-D array where values corresponds to the index in each row of
    sp_indices.
    default_value: values to set for indices not specified in sp_indices.
  Return:
    A dense numpy N-D array with shape output_shape.
  """

  assert len(sp_indices) == len(values), \
      'Length of sp_indices is not equal to length of values'

  array = np.ones(output_shape) * default_value
  for idx, value in zip(sp_indices, values):
    array[tuple(idx)] = value
  return array 
Example 6
Project: MKLMM   Author: omerwe   File: mklmm.py    (BSD 2-Clause "Simplified" License) View Source Project 6 votes vote down vote up
def infExact_scipy_post(self, K, covars, y, sig2e, fixedEffects):
		n = y.shape[0]

		#mean vector
		m = covars.dot(fixedEffects)
		
		if (K.shape[1] < K.shape[0]): K_true = K.dot(K.T)
		else: K_true = K
		
		if sig2e<1e-6:
			L = la.cholesky(K_true + sig2e*np.eye(n), overwrite_a=True, check_finite=False)    	 #Cholesky factor of covariance with noise
			sl =   1
			pL = -self.solveChol(L, np.eye(n))         									 		 #L = -inv(K+inv(sW^2))
		else:
			L = la.cholesky(K_true/sig2e + np.eye(n), overwrite_a=True, check_finite=False)	  	 #Cholesky factor of B
			sl = sig2e               	   
			pL = L                		   												 		 #L = chol(eye(n)+sW*sW'.*K)
		alpha = self.solveChol(L, y-m, overwrite_b=False) / sl
			
		post = dict([])	
		post['alpha'] = alpha					  										  		#return the posterior parameters
		post['sW'] = np.ones(n) / np.sqrt(sig2e)									  			#sqrt of noise precision vector
		post['L'] = pL
		return post 
Example 7
Project: MKLMM   Author: omerwe   File: kernels.py    (BSD 2-Clause "Simplified" License) View Source Project 6 votes vote down vote up
def getTrainTestKernel(self, params, Xtest):
		self.checkParams(params)
		ell = np.exp(params[0])
		p = np.exp(params[1])
		
		Xtest_scaled = Xtest/np.sqrt(Xtest.shape[1])
		d2 = sq_dist(self.X_scaled.T/ell, Xtest_scaled.T/ell)	#precompute squared distances
		
		#compute dp
		dp = np.zeros(d2.shape)
		for d in xrange(self.X_scaled.shape[1]):
			dp += (np.outer(self.X_scaled[:,d], np.ones((1, Xtest_scaled.shape[0]))) - np.outer(np.ones((self.X_scaled.shape[0], 1)), Xtest_scaled[:,d]))
		dp /= p
				
		K = np.exp(-d2 / 2.0)
		return np.cos(2*np.pi*dp)*K 
Example 8
Project: core-framework   Author: RedhawkSDR   File: plots.py    (license) View Source Project 6 votes vote down vote up
def __init__(self, frameSize=1024, imageWidth=1024, imageHeight=1024, zmin=-1, zmax=1, defaultValue=0.0):
        PlotBase.__init__(self)

        self._frameSize = frameSize
        self._sink = None

        # Raster state
        self._imageData = numpy.ones((imageHeight, imageWidth)) * defaultValue
        self._image = self._plot.imshow(self._imageData, extent=(0, 1, 1, 0))
        self._zmin = zmin
        self._zmax = zmax
        norm = self._getNorm(self._zmin, self._zmax)
        self._image.set_norm(norm)
        self._row = 0
        self._xdelta = None

        # Maintain aspect ratio of image
        self._aspect = float(imageHeight)/imageWidth
        self._plot.set_aspect(self._aspect)

        # Add a colorbar
        self._colorbar = self._figure.colorbar(self._image) 
Example 9
Project: seq2seq   Author: google   File: decoder_test.py    (license) View Source Project 6 votes vote down vote up
def test_with_fixed_inputs(self):
    inputs = tf.random_normal(
        [self.batch_size, self.sequence_length, self.input_depth])
    seq_length = tf.ones(self.batch_size, dtype=tf.int32) * self.sequence_length

    helper = decode_helper.TrainingHelper(
        inputs=inputs, sequence_length=seq_length)
    decoder_fn = self.create_decoder(
        helper=helper, mode=tf.contrib.learn.ModeKeys.TRAIN)
    initial_state = decoder_fn.cell.zero_state(
        self.batch_size, dtype=tf.float32)
    decoder_output, _ = decoder_fn(initial_state, helper)

    #pylint: disable=E1101
    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      decoder_output_ = sess.run(decoder_output)

    np.testing.assert_array_equal(
        decoder_output_.logits.shape,
        [self.sequence_length, self.batch_size, self.vocab_size])
    np.testing.assert_array_equal(decoder_output_.predicted_ids.shape,
                                  [self.sequence_length, self.batch_size])

    return decoder_output_ 
Example 10
Project: seq2seq   Author: google   File: pooling_encoder.py    (license) View Source Project 6 votes vote down vote up
def position_encoding(sentence_size, embedding_size):
  """
  Position Encoding described in section 4.1 of
  End-To-End Memory Networks (https://arxiv.org/abs/1503.08895).

  Args:
    sentence_size: length of the sentence
    embedding_size: dimensionality of the embeddings

  Returns:
    A numpy array of shape [sentence_size, embedding_size] containing
    the fixed position encodings for each sentence position.
  """
  encoding = np.ones((sentence_size, embedding_size), dtype=np.float32)
  ls = sentence_size + 1
  le = embedding_size + 1
  for k in range(1, le):
    for j in range(1, ls):
      encoding[j-1, k-1] = (1.0 - j/float(ls)) - (
          k / float(le)) * (1. - 2. * j/float(ls))
  return encoding 
Example 11
Project: zipline-chinese   Author: zhanghan1990   File: factory.py    (Apache License 2.0) View Source Project 6 votes vote down vote up
def create_test_panel_ohlc_source(sim_params, env):
    start = sim_params.first_open \
        if sim_params else pd.datetime(1990, 1, 3, 0, 0, 0, 0, pytz.utc)

    end = sim_params.last_close \
        if sim_params else pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)

    index = env.days_in_range(start, end)
    price = np.arange(0, len(index)) + 100
    high = price * 1.05
    low = price * 0.95
    open_ = price + .1 * (price % 2 - .5)
    volume = np.ones(len(index)) * 1000
    arbitrary = np.ones(len(index))

    df = pd.DataFrame({'price': price,
                       'high': high,
                       'low': low,
                       'open': open_,
                       'volume': volume,
                       'arbitrary': arbitrary},
                      index=index)
    panel = pd.Panel.from_dict({0: df})

    return DataPanelSource(panel), panel 
Example 12
Project: Deep360Pilot-optical-flow   Author: yenchenlin   File: HoF.py    (license) View Source Project 6 votes vote down vote up
def flow_orientation(orientation):
    """ Currently not use anymore """
    # Boolean map
    _greater_pi = orientation > math.pi/2
    _less_minuspi = orientation < -math.pi/2
    _remaining_part = ~(_greater_pi & _less_minuspi)

    # orientation map
    greater_pi = orientation*_greater_pi
    less_minuspi = orientation*_less_minuspi
    remaining_part = orientation*_remaining_part
    pi_map = math.pi * np.ones(orientation.shape)

    # converted orientation map
    convert_greater_pi = pi_map*_greater_pi - greater_pi
    convert_less_minuspi = -pi_map*_less_minuspi - less_minuspi

    new_orient = remaining_part + convert_greater_pi + convert_less_minuspi

    return new_orient 
Example 13
Project: bnn-analysis   Author: myshkov   File: sampler.py    (MIT License) View Source Project 6 votes vote down vote up
def _normalise_data(self):
        self.train_x_mean = np.zeros(self.input_dim)
        self.train_x_std = np.ones(self.input_dim)

        self.train_y_mean = np.zeros(self.output_dim)
        self.train_y_std = np.ones(self.output_dim)

        if self.normalise_data:
            self.train_x_mean = np.mean(self.train_x, axis=0)
            self.train_x_std = np.std(self.train_x, axis=0)
            self.train_x_std[self.train_x_std == 0] = 1.

            self.train_x = (self.train_x - np.full(self.train_x.shape, self.train_x_mean, dtype=np.float32)) / \
                           np.full(self.train_x.shape, self.train_x_std, dtype=np.float32)

            self.test_x = (self.test_x - np.full(self.test_x.shape, self.train_x_mean, dtype=np.float32)) / \
                          np.full(self.test_x.shape, self.train_x_std, dtype=np.float32)

            self.train_y_mean = np.mean(self.train_y, axis=0)
            self.train_y_std = np.std(self.train_y, axis=0)

            if self.train_y_std == 0:
                self.train_y_std[self.train_y_std == 0] = 1.

            self.train_y = (self.train_y - self.train_y_mean) / self.train_y_std 
Example 14
Project: pycpd   Author: siavashk   File: fishAffine3D.py    (MIT License) View Source Project 6 votes vote down vote up
def main():
    fish = loadmat('./data/fish.mat')

    X1 = np.zeros((fish['X'].shape[0], fish['X'].shape[1] + 1))
    X1[:,:-1] = fish['X']
    X2 = np.ones((fish['X'].shape[0], fish['X'].shape[1] + 1))
    X2[:,:-1] = fish['X']
    X = np.vstack((X1, X2))

    Y1 = np.zeros((fish['Y'].shape[0], fish['Y'].shape[1] + 1))
    Y1[:,:-1] = fish['Y']
    Y2 = np.ones((fish['Y'].shape[0], fish['Y'].shape[1] + 1))
    Y2[:,:-1] = fish['Y']
    Y = np.vstack((Y1, Y2))

    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    callback = partial(visualize, ax=ax)

    reg = affine_registration(X, Y)
    reg.register(callback)
    plt.show() 
Example 15
Project: alchemy   Author: voidrank   File: mask.py    (MIT License) View Source Project 6 votes vote down vote up
def test_bbs_in_bbs(self):
        bbs_a = np.array([1, 1, 2.0, 3])
        bbs_b = np.array([1, 0, 4, 5])
        bbs_c = np.array([0, 0, 2, 2])
        assert bbs_in_bbs(bbs_a, bbs_b).all()
        assert bbs_in_bbs(bbs_b, bbs_c).any() is not True
        assert bbs_in_bbs(bbs_a, bbs_c).any() is not True
        bbs_d = np.array([
            [0, 0, 5, 5],
            [1, 2, 4, 4],
            [2, 3, 4, 5]
            ])
        assert (bbs_in_bbs(bbs_a, bbs_d) == np.array([1, 0, 0], dtype=np.bool)).all()
        assert (bbs_in_bbs(bbs_d, bbs_d) == np.ones((3), dtype=np.bool)).all()
        bbs_a *= 100
        bbs_d *= 100
        assert (bbs_in_bbs(bbs_a, bbs_d) == np.array([1, 0, 0], dtype=np.bool)).all() 
Example 16
Project: MachineLearningProjects   Author: geallen   File: LR.py    (license) View Source Project 6 votes vote down vote up
def computeCost(X, y, theta):
    '''YOUR CODE HERE'''
    m = float(len(X))

    d = 0
    for i in range(len(X)):
        h = np.dot(theta.transpose(), X[i])
        c = (h - y[i])

        c = (c **2)
        d = (d + c)
    j = (1.0 / (2 * m)) * d
    return j


# Part 5: Prepare the data so that the input X has two columns: first a column of ones to accomodate theta0 and then a column of city population data 
Example 17
Project: cloud-volume   Author: seung-lab   File: test_cloudvolume.py    (BSD 3-Clause "New" or "Revised" License) View Source Project 6 votes vote down vote up
def test_write():
    delete_layer()
    cv, data = create_layer(size=(50,50,50,1), offset=(0,0,0))

    replacement_data = np.zeros(shape=(50,50,50,1), dtype=np.uint8)
    cv[0:50,0:50,0:50] = replacement_data
    assert np.all(cv[0:50,0:50,0:50] == replacement_data)

    replacement_data = np.random.randint(255, size=(50,50,50,1), dtype=np.uint8)
    cv[0:50,0:50,0:50] = replacement_data
    assert np.all(cv[0:50,0:50,0:50] == replacement_data)

    # out of bounds
    delete_layer()
    cv, data = create_layer(size=(128,64,64,1), offset=(10,20,0))
    with pytest.raises(ValueError):
        cv[74:150,20:84,0:64] = np.ones(shape=(64,64,64,1), dtype=np.uint8)
    
    # non-aligned writes
    delete_layer()
    cv, data = create_layer(size=(128,64,64,1), offset=(10,20,0))
    with pytest.raises(ValueError):
        cv[21:85,0:64,0:64] = np.ones(shape=(64,64,64,1), dtype=np.uint8) 
Example 18
Project: vae-npvc   Author: JeremyCCHsu   File: analyzer.py    (license) View Source Project 6 votes vote down vote up
def extract_and_save_bin_to(dir_to_bin, dir_to_source):
    sets = [s for s in os.listdir(dir_to_source) if s in SETS]
    for d in sets:
        path = join(dir_to_source, d)
        speakers = [s for s in os.listdir(path) if s in SPEAKERS]
        for s in speakers:
            path = join(dir_to_source, d, s)
            output_dir = join(dir_to_bin, d, s)
            if not tf.gfile.Exists(output_dir):
                tf.gfile.MakeDirs(output_dir)
            for f in os.listdir(path):
                filename = join(path, f)
                print(filename)
                if not os.path.isdir(filename):
                    features = extract(filename)
                    labels = SPEAKERS.index(s) * np.ones(
                        [features.shape[0], 1],
                        np.float32,
                    )
                    b = os.path.splitext(f)[0]
                    features = np.concatenate([features, labels], 1)
                    with open(join(output_dir, '{}.bin'.format(b)), 'wb') as fp:
                        fp.write(features.tostring()) 
Example 19
Project: pybot   Author: spillai   File: rigid_transform.py    (license) View Source Project 6 votes vote down vote up
def __mul__(self, other):
        """ 
        Left-multiply RigidTransform with another rigid transform
        
        Two variants: 
           RigidTransform: Identical to oplus operation
           ndarray: transform [N x 3] point set (X_2 = p_21 * X_1)

        """
        if isinstance(other, DualQuaternion):
            return DualQuaternion.from_dq(other.real * self.real, 
                                          other.dual * self.real + other.real * self.dual)
        elif isinstance(other, float):
            return DualQuaternion.from_dq(self.real * other, self.dual * other)
        # elif isinstance(other, nd.array): 
        #     X = np.hstack([other, np.ones((len(other),1))]).T
        #     return (np.dot(self.matrix, X).T)[:,:3]
        else: 
            raise TypeError('__mul__ typeerror {:}'.format(type(other))) 
Example 20
Project: a-nice-mc   Author: ermongroup   File: evaluation.py    (MIT License) View Source Project 6 votes vote down vote up
def effective_sample_size(x, mu, var, logger):
    """
    Calculate the effective sample size of sequence generated by MCMC.
    :param x:
    :param mu: mean of the variable
    :param var: variance of the variable
    :param logger: logg
    :return: effective sample size of the sequence
    Make sure that `mu` and `var` are correct!
    """
    # batch size, time, dimension
    b, t, d = x.shape
    ess_ = np.ones([d])
    for s in range(1, t):
        p = auto_correlation_time(x, s, mu, var)
        if np.sum(p > 0.05) == 0:
            break
        else:
            for j in range(0, d):
                if p[j] > 0.05:
                    ess_[j] += 2.0 * p[j] * (1.0 - float(s) / t)

    logger.info('ESS: max [%f] min [%f] / [%d]' % (t / np.min(ess_), t / np.max(ess_), t))
    return t / ess_ 
Example 21
Project: Python-Machine-Learning-By-Example   Author: PacktPublishing   File: 2linear_regression.py    (MIT License) View Source Project 6 votes vote down vote up
def train_linear_regression(X_train, y_train, max_iter, learning_rate, fit_intercept=False):
    """ Train a linear regression model with gradient descent
    Args:
        X_train, y_train (numpy.ndarray, training data set)
        max_iter (int, number of iterations)
        learning_rate (float)
        fit_intercept (bool, with an intercept w0 or not)
    Returns:
        numpy.ndarray, learned weights
    """
    if fit_intercept:
        intercept = np.ones((X_train.shape[0], 1))
        X_train = np.hstack((intercept, X_train))
    weights = np.zeros(X_train.shape[1])
    for iteration in range(max_iter):
        weights = update_weights_gd(X_train, y_train, weights, learning_rate)
        # Check the cost for every 100 (for example) iterations
        if iteration % 100 == 0:
            print(compute_cost(X_train, y_train, weights))
    return weights 
Example 22
Project: Python-Machine-Learning-By-Example   Author: PacktPublishing   File: 3logistic_regression_from_scratch.py    (MIT License) View Source Project 6 votes vote down vote up
def train_logistic_regression(X_train, y_train, max_iter, learning_rate, fit_intercept=False):
    """ Train a logistic regression model
    Args:
        X_train, y_train (numpy.ndarray, training data set)
        max_iter (int, number of iterations)
        learning_rate (float)
        fit_intercept (bool, with an intercept w0 or not)
    Returns:
        numpy.ndarray, learned weights
    """
    if fit_intercept:
        intercept = np.ones((X_train.shape[0], 1))
        X_train = np.hstack((intercept, X_train))
    weights = np.zeros(X_train.shape[1])
    for iteration in range(max_iter):
        weights = update_weights_gd(X_train, y_train, weights, learning_rate)
        # Check the cost for every 100 (for example) iterations
        if iteration % 1000 == 0:
            print(compute_cost(X_train, y_train, weights))
    return weights 
Example 23
Project: Python-Machine-Learning-By-Example   Author: PacktPublishing   File: 3logistic_regression_from_scratch.py    (MIT License) View Source Project 6 votes vote down vote up
def train_logistic_regression(X_train, y_train, max_iter, learning_rate, fit_intercept=False):
    """ Train a logistic regression model
    Args:
        X_train, y_train (numpy.ndarray, training data set)
        max_iter (int, number of iterations)
        learning_rate (float)
        fit_intercept (bool, with an intercept w0 or not)
    Returns:
        numpy.ndarray, learned weights
    """
    if fit_intercept:
        intercept = np.ones((X_train.shape[0], 1))
        X_train = np.hstack((intercept, X_train))
    weights = np.zeros(X_train.shape[1])
    for iteration in range(max_iter):
        weights = update_weights_sgd(X_train, y_train, weights, learning_rate)
        # Check the cost for every 2 (for example) iterations
        if iteration % 2 == 0:
            print(compute_cost(X_train, y_train, weights))
    return weights


# Train the SGD model based on 10000 samples 
Example 24
Project: pycma   Author: CMA-ES   File: sampler.py    (license) View Source Project 6 votes vote down vote up
def norm(self, x):
        """compute the Mahalanobis norm that is induced by the
        statistical model / sample distribution, specifically by
        covariance matrix ``C``. The expected Mahalanobis norm is
        about ``sqrt(dimension)``.

        Example
        -------
        >>> import cma, numpy as np
        >>> sm = cma.sampler.GaussFullSampler(np.ones(10))
        >>> x = np.random.randn(10)
        >>> d = sm.norm(x)

        `d` is the norm "in" the true sample distribution,
        sampled points have a typical distance of ``sqrt(2*sm.dim)``,
        where ``sm.dim`` is the dimension, and an expected distance of
        close to ``dim**0.5`` to the sample mean zero. In the example,
        `d` is the Euclidean distance, because C = I.
        """
        return sum((np.dot(self.B.T, x) / self.D)**2)**0.5 
Example 25
Project: pycma   Author: CMA-ES   File: sampler.py    (license) View Source Project 6 votes vote down vote up
def __init__(self, dimension,
                 constant_trace='None',
                 randn=np.random.randn,
                 quadratic=False,
                 **kwargs):
        try:
            self.dimension = len(dimension)
            standard_deviations = np.asarray(dimension)
        except TypeError:
            self.dimension = dimension
            standard_deviations = np.ones(dimension)
        assert self.dimension == len(standard_deviations)
        assert len(standard_deviations) == self.dimension

        self.C = standard_deviations**2
        "covariance matrix diagonal"
        self.constant_trace = constant_trace
        self.randn = randn
        self.quadratic = quadratic
        self.count_tell = 0 
Example 26
Project: pycma   Author: CMA-ES   File: sampler.py    (license) View Source Project 6 votes vote down vote up
def norm(self, x):
        """compute the Mahalanobis norm that is induced by the
        statistical model / sample distribution, specifically by
        covariance matrix ``C``. The expected Mahalanobis norm is
        about ``sqrt(dimension)``.

        Example
        -------
        >>> import cma, numpy as np
        >>> sm = cma.sampler.GaussFullSampler(np.ones(10))
        >>> x = np.random.randn(10)
        >>> d = sm.norm(x)

        `d` is the norm "in" the true sample distribution,
        sampled points have a typical distance of ``sqrt(2*sm.dim)``,
        where ``sm.dim`` is the dimension, and an expected distance of
        close to ``dim**0.5`` to the sample mean zero. In the example,
        `d` is the Euclidean distance, because C = I.
        """
        return sum(np.asarray(x)**2 / self.C)**0.5 
Example 27
Project: pycma   Author: CMA-ES   File: restricted_gaussian_sampler.py    (license) View Source Project 6 votes vote down vote up
def __init__(self, dimension, randn=np.random.randn, debug=False):
        """pass dimension of the underlying sample space
        """
        try:
            self.N = len(dimension)
            std_vec = np.array(dimension, copy=True)
        except TypeError:
            self.N = dimension
            std_vec = np.ones(self.N)
        if self.N < 10:
            print('Warning: Not advised to use VD-CMA for dimension < 10.')
        self.randn = randn
        self.dvec = std_vec
        self.vvec = self.randn(self.N) / math.sqrt(self.N)
        self.norm_v2 = np.dot(self.vvec, self.vvec)
        self.norm_v = np.sqrt(self.norm_v2)
        self.vn = self.vvec / self.norm_v
        self.vnn = self.vn**2
        self.pc = np.zeros(self.N)
        self._debug = debug  # plot covariance matrix 
Example 28
Project: pycma   Author: CMA-ES   File: restricted_gaussian_sampler.py    (license) View Source Project 6 votes vote down vote up
def covariance_matrix(self):
        if self._debug:
            # return None
            ka = self.k_active
            if ka > 0:
                C = np.eye(self.N) + np.dot(self.V[:ka].T * self.S[:ka],
                                            self.V[:ka])
                C = (C * self.D).T * self.D
            else:
                C = np.diag(self.D**2)
            C *= self.sigma**2
        else:
            # Fake Covariance Matrix for Speed
            C = np.ones(1)
            self.B = np.ones(1)
        return C 
Example 29
Project: pycma   Author: CMA-ES   File: bbobbenchmarks.py    (license) View Source Project 6 votes vote down vote up
def initwithsize(self, curshape, dim):
        # DIM-dependent initialization
        if self.dim != dim:
            if self.zerox:
                self.xopt = zeros(dim)
            else:
                self.xopt = compute_xopt(self.rseed, dim)
            scale = max(1, dim ** .5 / 8.) # nota: different from scales in F8
            self.linearTF = scale * compute_rotation(self.rseed, dim)
            self.xopt = np.hstack(dot(.5 * np.ones((1, dim)), self.linearTF.T)) / scale ** 2

        # DIM- and POPSI-dependent initialisations of DIM*POPSI matrices
        if self.lastshape != curshape:
            self.dim = dim
            self.lastshape = curshape
            self.arrxopt = resize(self.xopt, curshape) 
Example 30
Project: pycma   Author: CMA-ES   File: transformations.py    (license) View Source Project 6 votes vote down vote up
def __imul__(self, factor):
        """define ``self *= factor``.

        As a shortcut for::

            self = self.__imul__(factor)

        """
        try:
            if factor == 1:
                return self
        except: pass
        try:
            if (np.size(factor) == np.size(self.scaling) and
                    all(factor == 1)):
                return self
        except: pass
        if self.is_identity and np.size(self.scaling) == 1:
            self.scaling = np.ones(np.size(factor))
        self.is_identity = False
        self.scaling *= factor
        self.dim = np.size(self.scaling)
        return self 
Example 31
Project: slim-python   Author: ustunb   File: SLIMCoefficientConstraints.py    (GNU General Public License v3.0) View Source Project 6 votes vote down vote up
def check_string_input(self, input_name, input_value):
        if type(input_value) is np.array:

            if input_value.size == self.P:
                setattr(self, input_name, input_value)
            elif input_value.size == 1:
                setattr(self, input_name, np.repeat(input_value, self.P))
            else:
                raise ValueError("length of %s is %d; should be %d" % (input_name, input_value.size, self.P))

        elif type(input_value) is str:
            setattr(self, input_name, float(input_value)*np.ones(self.P))

        elif type(input_value) is list:
            if len(input_value) == self.P:
                setattr(self, input_name, np.array([str(x) for x in input_value]))
            elif len(input_value) == 1:
                setattr(self, input_name, np.repeat(input_value, self.P))
            else:
                raise ValueError("length of %s is %d; should be %d" % (input_name, len(input_value), self.P))

        else:
            raise ValueError("user provided %s with an unsupported type" % input_name) 
Example 32
Project: slim-python   Author: ustunb   File: SLIMCoefficientConstraints.py    (GNU General Public License v3.0) View Source Project 6 votes vote down vote up
def check_numeric_input(self, input_name, input_value):
        if type(input_value) is np.ndarray:

            if input_value.size == self.P:
                setattr(self, input_name, input_value)
            elif input_value.size == 1:
                setattr(self, input_name, input_value*np.ones(self.P))
            else:
                raise ValueError("length of %s is %d; should be %d" % (input_name, input_value.size, self.P))

        elif type(input_value) is float or type(input_value) is int:
            setattr(self, input_name, float(input_value)*np.ones(self.P))

        elif type(input_value) is list:
            if len(input_value) == self.P:
                setattr(self, input_name, np.array([float(x) for x in input_value]))
            elif len(input_value) == 1:
                setattr(self, input_name, np.array([float(x) for x in input_value]) * np.ones(self.P))
            else:
                raise ValueError("length of %s is %d; should be %d" % (input_name, len(input_value), self.P))

        else:
            raise ValueError("user provided %s with an unsupported type" % (input_name)) 
Example 33
Project: convolutional-pose-machines-tensorflow   Author: timctho   File: cpm_utils.py    (Apache License 2.0) View Source Project 6 votes vote down vote up
def make_heatmaps_from_joints(input_size, heatmap_size, gaussian_variance, batch_joints):
    # Generate ground-truth heatmaps from ground-truth 2d joints
    scale_factor = input_size // heatmap_size
    batch_gt_heatmap_np = []
    for i in range(batch_joints.shape[0]):
        gt_heatmap_np = []
        invert_heatmap_np = np.ones(shape=(heatmap_size, heatmap_size))
        for j in range(batch_joints.shape[1]):
            cur_joint_heatmap = make_gaussian(heatmap_size,
                                              gaussian_variance,
                                              center=(batch_joints[i][j] // scale_factor))
            gt_heatmap_np.append(cur_joint_heatmap)
            invert_heatmap_np -= cur_joint_heatmap
        gt_heatmap_np.append(invert_heatmap_np)
        batch_gt_heatmap_np.append(gt_heatmap_np)
    batch_gt_heatmap_np = np.asarray(batch_gt_heatmap_np)
    batch_gt_heatmap_np = np.transpose(batch_gt_heatmap_np, (0, 2, 3, 1))

    return batch_gt_heatmap_np 
Example 34
Project: psola   Author: jcreinhold   File: estimation.py    (MIT License) View Source Project 6 votes vote down vote up
def primes_2_to_n(n):
    """
    Efficient algorithm to find and list primes from
    2 to `n'.

    Args:
        n (int): highest number from which to search for primes

    Returns:
        np array of all primes from 2 to n

    References:
        Robert William Hanks,
        https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n/
    """
    sieve = np.ones(int(n / 3 + (n % 6 == 2)), dtype=np.bool)
    for i in range(1, int((n ** 0.5) / 3 + 1)):
        if sieve[i]:
            k = 3 * i + 1 | 1
            sieve[int(k * k / 3)::2 * k] = False
            sieve[int(k * (k - 2 * (i & 1) + 4) / 3)::2 * k] = False
    return np.r_[2, 3, ((3 * np.nonzero(sieve)[0][1:] + 1) | 1)] 
Example 35
Project: CausalGAN   Author: mkocaoglu   File: distributions.py    (MIT License) View Source Project 6 votes vote down vote up
def get_interv_table(model,intrv=True):

    n_batches=25
    table_outputs=[]
    d_vals=np.linspace(TINY,0.6,n_batches)
    for name in model.cc.node_names:
        outputs=[]
        for d_val in d_vals:
            do_dict={model.cc.node_dict[name].label_logit : d_val*np.ones((model.batch_size,1))}
            outputs.append(model.sess.run(model.fake_labels,do_dict))

        out=np.vstack(outputs)
        table_outputs.append(out)

    table=np.stack(table_outputs,axis=2)

    np.mean(np.round(table),axis=0)

    return table

#dT=pd.DataFrame(index=p_names, data=T, columns=do_names)
#T=np.mean(np.round(table),axis=0)
#table=get_interv_table(model) 
Example 36
Project: NeoAnalysis   Author: neoanalysis   File: colormap.py    (license) View Source Project 6 votes vote down vote up
def __init__(self, pos, color, mode=None):
        """
        ===============     ==============================================================
        **Arguments:**
        pos                 Array of positions where each color is defined
        color               Array of RGBA colors.
                            Integer data types are interpreted as 0-255; float data types
                            are interpreted as 0.0-1.0
        mode                Array of color modes (ColorMap.RGB, HSV_POS, or HSV_NEG)
                            indicating the color space that should be used when
                            interpolating between stops. Note that the last mode value is
                            ignored. By default, the mode is entirely RGB.
        ===============     ==============================================================
        """
        self.pos = np.array(pos)
        order = np.argsort(self.pos)
        self.pos = self.pos[order]
        self.color = np.array(color)[order]
        if mode is None:
            mode = np.ones(len(pos))
        self.mode = mode
        self.stopsCache = {} 
Example 37
Project: NeoAnalysis   Author: neoanalysis   File: test_pynnio.py    (license) View Source Project 6 votes vote down vote up
def build_test_data(self, variable='v'):
        metadata = {
            'size': NCELLS,
            'first_index': 0,
            'first_id': 0,
            'n': 505,
            'variable': variable,
            'last_id': NCELLS - 1,
            'last_index': NCELLS - 1,
            'dt': 0.1,
            'label': "population0",
        }
        if variable == 'v':
            metadata['units'] = 'mV'
        elif variable == 'spikes':
            metadata['units'] = 'ms'
        data = np.empty((505, 2))
        for i in range(NCELLS):
            # signal
            data[i*101:(i+1)*101, 0] = np.arange(i, i+101, dtype=float)
            # index
            data[i*101:(i+1)*101, 1] = i*np.ones((101,), dtype=float)
        return data, metadata 
Example 38
Project: NeoAnalysis   Author: neoanalysis   File: colormap.py    (license) View Source Project 6 votes vote down vote up
def __init__(self, pos, color, mode=None):
        """
        ===============     ==============================================================
        **Arguments:**
        pos                 Array of positions where each color is defined
        color               Array of RGBA colors.
                            Integer data types are interpreted as 0-255; float data types
                            are interpreted as 0.0-1.0
        mode                Array of color modes (ColorMap.RGB, HSV_POS, or HSV_NEG)
                            indicating the color space that should be used when
                            interpolating between stops. Note that the last mode value is
                            ignored. By default, the mode is entirely RGB.
        ===============     ==============================================================
        """
        self.pos = np.array(pos)
        order = np.argsort(self.pos)
        self.pos = self.pos[order]
        self.color = np.array(color)[order]
        if mode is None:
            mode = np.ones(len(pos))
        self.mode = mode
        self.stopsCache = {} 
Example 39
Project: NeoAnalysis   Author: neoanalysis   File: test_pynnio.py    (license) View Source Project 6 votes vote down vote up
def build_test_data(self, variable='v'):
        metadata = {
            'size': NCELLS,
            'first_index': 0,
            'first_id': 0,
            'n': 505,
            'variable': variable,
            'last_id': NCELLS - 1,
            'last_index': NCELLS - 1,
            'dt': 0.1,
            'label': "population0",
        }
        if variable == 'v':
            metadata['units'] = 'mV'
        elif variable == 'spikes':
            metadata['units'] = 'ms'
        data = np.empty((505, 2))
        for i in range(NCELLS):
            # signal
            data[i*101:(i+1)*101, 0] = np.arange(i, i+101, dtype=float)
            # index
            data[i*101:(i+1)*101, 1] = i*np.ones((101,), dtype=float)
        return data, metadata 
Example 40
Project: pyku   Author: dubvulture   File: utils.py    (GNU General Public License v3.0) View Source Project 5 votes vote down vote up
def ONES(n):
    return np.ones((n, n), np.uint8) 
Example 41
Project: TDOSE   Author: kasperschmidt   File: tdose_utilities.py    (MIT License) View Source Project 5 votes vote down vote up
def gen_noisy_cube(cube,type='poisson',gauss_std=0.5,verbose=True):
    """
    Generate noisy cube based on input cube.

    --- INPUT ---
    cube        Data cube to be smoothed
    type        Type of noise to generate
                  poisson    Generates poissonian (integer) noise
                  gauss      Generates gaussian noise for a gaussian with standard deviation gauss_std=0.5
    gauss_std   Standard deviation of noise if type='gauss'
    verbose     Toggle verbosity

    --- EXAMPLE OF USE ---
    import tdose_utilities as tu
    datacube        = np.ones(([3,3,3])); datacube[0,1,1]=5; datacube[1,1,1]=6; datacube[2,1,1]=8
    cube_with_noise = tu.gen_noisy_cube(datacube,type='gauss',gauss_std='0.5')

    """
    if verbose: print ' - Generating "'+type+'" noise on data cube'
    if type == 'poisson':
        cube_with_noise = np.random.poisson(lam=cube, size=None)
    elif type == 'gauss':
        cube_with_noise = cube + np.random.normal(loc=np.zeros(cube.shape),scale=gauss_std, size=None)
    else:
        sys.exit(' ---> type="'+type+'" is not valid in call to mock_cube_sources.generate_cube_noise() ')

    return cube_with_noise
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
Example 42
Project: drl.pth   Author: seba-1511   File: env_converter.py    (license) View Source Project 5 votes vote down vote up
def __init__(self, env, shape, clip=10.0, update_freq=100):
        self.env = env
        self.clip = clip
        self.update_freq = update_freq
        self.count = 0
        self.sum = 0.0
        self.sum_sqr = 0.0
        self.mean = np.zeros(shape, dtype=np.double)
        self.std = np.ones(shape, dtype=np.double) 
Example 43
Project: pylspm   Author: lseman   File: fimix.py    (MIT License) View Source Project 5 votes vote down vote up
def expectation(self, dataSplit, coefficients, variances):

        assignment_weights = np.ones(
            (len(dataSplit), self.num_components), dtype=float)

        self.Q = len(self.endoVar)

        for k in range(self.num_components):

            coef_ = coefficients[k]

            Beta = coef_.ix[self.endoVar][self.endoVar]
            Gamma = coef_.ix[self.endoVar][self.exoVar]

            a_ = (np.dot(Beta, self.fscores[
                  self.endoVar].T) + np.dot(Gamma, self.fscores[self.exoVar].T))

            invert_ = np.linalg.inv(np.array(variances[k]))

            exponential = np.exp(-0.5 * np.dot(np.dot(a_.T, invert_), a_))

            den = (((2 * np.pi)**(self.Q / 2)) *
                   np.sqrt(np.linalg.det(variances[k])))
            probabilities = exponential / den
            probabilities = probabilities[0]
            assignment_weights[:, k] = probabilities

        assignment_weights /= assignment_weights.sum(axis=1)[:, np.newaxis]
 #       print(assignment_weights)
        return assignment_weights 
Example 44
Project: pyfds   Author: emtpb   File: nonlinear_acoustics.py    (BSD 3-Clause "New" or "Revised" License) View Source Project 5 votes vote down vote up
def create_matrices(self):
        """Creates the a_* matrices required for simulation."""

        self.a_d_v = self.d_x(factors=(self.t.increment / self.x.increment *
                                       np.ones(self.x.samples)))
        self.a_v_p = self.d_x(factors=(self.t.increment / self.x.increment) *
                              np.ones(self.x.samples), variant='backward')
        self.a_v_v = self.d_x2(factors=(self.t.increment / self.x.increment ** 2 *
                                        self.material_vector('absorption_coef')))
        self.a_v_v2 = self.d_x(factors=(self.t.increment / self.x.increment / 2) *
                               np.ones(self.x.samples), variant='central') 
Example 45
Project: pyfds   Author: emtpb   File: test_fields.py    (BSD 3-Clause "New" or "Revised" License) View Source Project 5 votes vote down vote up
def test_field_component_boundary_2():
    fc = fls.FieldComponent(100)
    fc.values = np.ones(100)
    fc.boundaries = [reg.Boundary(reg.LineRegion([5, 6, 7], [0, 0.2], 'test boundary'))]
    fc.boundaries[0].value = [23, 42, 23]
    fc.boundaries[0].additive = True
    fc.apply_bounds(step=0)
    assert np.allclose(fc.values[[5, 6, 7]], [24, 43, 24]) 
Example 46
Project: treecat   Author: posterior   File: profile.py    (Apache License 2.0) View Source Project 5 votes vote down vote up
def serve_files(model_path, config_path, num_samples):
    """INTERNAL Serve from pickled model, config."""
    from treecat.serving import TreeCatServer
    import numpy as np
    model = pickle_load(model_path)
    config = pickle_load(config_path)
    model['config'] = config
    server = TreeCatServer(model)
    counts = np.ones(model['tree'].num_vertices, np.int8)
    samples = server.sample(int(num_samples), counts)
    server.logprob(samples)
    server.median(counts, samples)
    server.latent_correlation() 
Example 47
Project: treecat   Author: posterior   File: serving_test.py    (Apache License 2.0) View Source Project 5 votes vote down vote up
def validate_gof(N, V, C, M, server, conditional):
    # Generate samples.
    expected = C**V
    num_samples = 1000 * expected
    ones = np.ones(V, dtype=np.int8)
    if conditional:
        cond_data = server.sample(1, ones)[0, :]
    else:
        cond_data = server.make_zero_row()
    samples = server.sample(num_samples, ones, cond_data)
    logprobs = server.logprob(samples + cond_data[np.newaxis, :])
    counts = {}
    probs = {}
    for sample, logprob in zip(samples, logprobs):
        key = tuple(sample)
        if key in counts:
            counts[key] += 1
        else:
            counts[key] = 1
            probs[key] = np.exp(logprob)
    assert len(counts) == expected

    # Check accuracy using Pearson's chi-squared test.
    keys = sorted(counts.keys(), key=lambda key: -probs[key])
    counts = np.array([counts[k] for k in keys], dtype=np.int32)
    probs = np.array([probs[k] for k in keys])
    probs /= probs.sum()

    # Truncate to avoid low-precision.
    truncated = False
    valid = (probs * num_samples > 20)
    if not valid.all():
        T = valid.argmin()
        T = max(8, T)  # Avoid truncating too much
        probs = probs[:T]
        counts = counts[:T]
        truncated = True

    gof = multinomial_goodness_of_fit(
        probs, counts, num_samples, plot=True, truncated=truncated)
    assert 1e-2 < gof 
Example 48
Project: treecat   Author: posterior   File: serving.py    (Apache License 2.0) View Source Project 5 votes vote down vote up
def sample_tree(self, num_samples):
        size = len(self._ensemble)
        pvals = np.ones(size, dtype=np.float32) / size
        sub_nums = np.random.multinomial(num_samples, pvals)
        samples = []
        for server, sub_num in zip(self._ensemble, sub_nums):
            samples += server.sample_tree(sub_num)
        np.random.shuffle(samples)
        assert len(samples) == num_samples
        return samples 
Example 49
Project: treecat   Author: posterior   File: serving.py    (Apache License 2.0) View Source Project 5 votes vote down vote up
def sample(self, N, counts, data=None):
        size = len(self._ensemble)
        pvals = np.ones(size, dtype=np.float32) / size
        sub_Ns = np.random.multinomial(N, pvals)
        samples = np.concatenate([
            server.sample(sub_N, counts, data)
            for server, sub_N in zip(self._ensemble, sub_Ns)
        ])
        np.random.shuffle(samples)
        assert samples.shape[0] == N
        return samples 
Example 50
Project: RasterFairy   Author: Quasimondo   File: rasterfairy.py    (BSD 3-Clause "New" or "Revised" License) View Source Project 5 votes vote down vote up
def arrangementToRasterMask( arrangement ):
    rows = np.array(arrangement['rows'])
    width = np.max(rows)
    if arrangement['hex'] is True:
        width+=1
    height = len(rows)
    mask = np.ones((height,width),dtype=int)
    for row in range(len(rows)):
        c = rows[row]
        mask[row,(width-c)>>1:((width-c)>>1)+c] = 0

    return {'width':width,'height':height,'mask':mask, 'count':np.sum(rows),'hex':arrangement['hex'],'type':arrangement['type']}