Python numpy 模块，zeros_like() 实例源码

def roi(img,vertices):
    # blank mask:
    mask = np.zeros_like(img)

    # filling pixels inside the polygon defined by "vertices" with the fill color
    cv2.fillPoly(mask, vertices, 255)

    # returning the image only where mask pixels are nonzero
    masked = cv2.bitwise_and(img, mask)
    return masked

def roll_zeropad(a, shift, axis=None):
    a = np.asanyarray(a)
    if shift == 0: return a
    if axis is None:
        n = a.size
        reshape = True
    else:
        n = a.shape[axis]
        reshape = False
    if np.abs(shift) > n:
        res = np.zeros_like(a)
    elif shift < 0:
        shift += n
        zeros = np.zeros_like(a.take(np.arange(n-shift), axis))
        res = np.concatenate((a.take(np.arange(n-shift,n), axis), zeros), axis)
    else:
        zeros = np.zeros_like(a.take(np.arange(n-shift,n), axis))
        res = np.concatenate((zeros, a.take(np.arange(n-shift), axis)), axis)
    if reshape:
        return res.reshape(a.shape)
    else:
        return res

def test_op(self):
    logits = np.random.randn(self.sequence_length, self.batch_size,
                             self.vocab_size)
    logits = logits.astype(np.float32)
    sequence_length = np.array([1, 2, 3, 4])
    targets = np.random.randint(0, self.vocab_size,
                                [self.sequence_length, self.batch_size])
    losses = seq2seq_losses.cross_entropy_sequence_loss(logits, targets,
                                                        sequence_length)

    with self.test_session() as sess:
      losses_ = sess.run(losses)

    # Make sure all losses not past the sequence length are > 0
    np.testing.assert_array_less(np.zeros_like(losses_[:1, 0]), losses_[:1, 0])
    np.testing.assert_array_less(np.zeros_like(losses_[:2, 1]), losses_[:2, 1])
    np.testing.assert_array_less(np.zeros_like(losses_[:3, 2]), losses_[:3, 2])

    # Make sure all losses past the sequence length are 0
    np.testing.assert_array_equal(losses_[1:, 0], np.zeros_like(losses_[1:, 0]))
    np.testing.assert_array_equal(losses_[2:, 1], np.zeros_like(losses_[2:, 1]))
    np.testing.assert_array_equal(losses_[3:, 2], np.zeros_like(losses_[3:, 2]))

def __init__(self, input_shape, output_shape):
        self.input_shape = input_shape
        self.input = np.zeros((output_shape[0], self.input_shape[0] * self.input_shape[1] *
                               self.input_shape[2]),dtype=np.float32)
        self.output = np.zeros(output_shape, dtype=np.float32)
        self.output_raw = np.zeros_like(self.output)
        self.output_error = np.zeros_like(self.output)
        self.output_average = np.zeros(self.output.shape[1], dtype=np.float32)
        self.weights = np.random.normal(0, np.sqrt(2.0 / (self.output.shape[1] + self.input.shape[1])),
                                        size=(self.input.shape[1], self.output.shape[1])).astype(np.float32)
        self.gradient = np.zeros_like(self.weights)
        self.reconstruction = np.zeros_like(self.weights)
        self.errors = np.zeros_like(self.weights)
        self.output_ranks = np.zeros(self.output.shape[1], dtype=np.int32)
        self.learning_rate = 1
        self.norm_limit = 0.1

def gen_batches(data, n_seqs, n_steps):
    """Create a generator that returns batches of size n_seqs x n_steps."""

    characters_per_batch = n_seqs * n_steps
    n_batches = len(data) // characters_per_batch

    # Keep only enough characters to make full batches
    data = data[:n_batches*characters_per_batch]
    data = data.reshape([n_seqs, -1])

    for n in range(0, data.shape[1], n_steps):
        x = data[:, n:n+n_steps]
        y = np.zeros_like(x)
        y[:, :-1], y[:, -1] = x[:, 1:], x[:, 0]
        yield x, y

#-------------------------------------------------------------------------------
# Parse commandline
#-------------------------------------------------------------------------------

def filter(self, p):
        """

        Parameters
        ----------
        p: NDArray
            Filtering input which is 2D or 3D with format
            HW or HWC

        Returns
        -------
        ret: NDArray
            Filtering output whose shape is same with input
        """
        p = to_32F(p)
        if len(p.shape) == 2:
            return self._Filter.filter(p)
        elif len(p.shape) == 3:
            channels = p.shape[2]
            ret = np.zeros_like(p, dtype=np.float32)
            for c in range(channels):
                ret[:, :, c] = self._Filter.filter(p[:, :, c])
            return ret

def _process_label(self, fn): 
        """
        TODO: Fix one-indexing to zero-index; 
        retained one-index due to uint8 constraint
        """
        mat = loadmat(fn, squeeze_me=True)
        _labels = mat['seglabel'].astype(np.uint8)
        # _labels -= 1 # (move to zero-index)

        labels = np.zeros_like(_labels)
        for (idx, name) in enumerate(mat['names']): 
            try: 
                value = SUNRGBDDataset.target_hash[name]
            except: 
                value = 0
            mask = _labels == idx+1
            labels[mask] = value
        return self._pad_image(labels)

def recall_from_IoU(IoU, samples=500): 
    """
    plot recall_vs_IoU_threshold
    """

    if not (isinstance(IoU, list) or IoU.ndim == 1):
        raise ValueError('IoU needs to be a list or 1-D')
    iou = np.float32(IoU)

    # Plot intersection over union
    IoU_thresholds = np.linspace(0.0, 1.0, samples)
    recall = np.zeros_like(IoU_thresholds)
    for idx, IoU_th in enumerate(IoU_thresholds):
        tp, relevant = 0, 0
        inds, = np.where(iou >= IoU_th)
        recall[idx] = len(inds) * 1.0 / len(IoU)

    return recall, IoU_thresholds 

# =====================================================================
# Generic utility functions for object recognition
# ---------------------------------------------------------------------

def reset_index(self):
        """Reset index to range based
        """
        dfs = self.to_delayed()
        sizes = np.asarray(compute(*map(delayed(len), dfs)))
        prefixes = np.zeros_like(sizes)
        prefixes[1:] = np.cumsum(sizes[:-1])

        @delayed
        def fix_index(df, startpos):
            return df.set_index(np.arange(start=startpos,
                                          stop=startpos + len(df),
                                          dtype=np.intp))

        outdfs = [fix_index(df, startpos)
                  for df, startpos in zip(dfs, prefixes)]
        return from_delayed(outdfs)

def recoded_features(self, inputs, layer=-1, inverse_fn=ielu):

        hidden = self.get_hidden_values(inputs, store=True, layer=layer).eval()

        bench = self.get_reconstructed_input(np.zeros_like(hidden),
                                             layer=layer).eval().squeeze()
        if inverse_fn: ibench = inverse_fn(bench)

        results = []
        for h in range(hidden.shape[-1]):
            hidden_h = np.zeros_like(hidden)
            hidden_h[..., h] = hidden[..., h]
            feature = self.get_reconstructed_input(hidden_h, layer=layer).eval().squeeze()
            if inverse_fn:
                iresult = inverse_fn(feature) - ibench
                results.append(self.coders[0].coding(iresult).eval())
            else:
                results.append(feature - bench)

        return np.array(results), bench

def zscore(x):
    """Computes the Z-score of a vector x. Removes the mean and divides by the
    standard deviation. Has a failback if std is 0 to return all zeroes.

    Parameters
    ----------
    x: list of int
        Input time-series

    Returns
    -------
    z: list of float
        Z-score normalized time-series
    """
    mean = np.mean(x)
    sd = np.std(x)
    if sd == 0:
        z = np.zeros_like(x)
    else:
        z = (x - mean)/sd
    return z

def _encode_observation(self, idx):
        end_idx   = idx + 1 # make noninclusive
        start_idx = end_idx - self.frame_history_len
        # this checks if we are using low-dimensional observations, such as RAM
        # state, in which case we just directly return the latest RAM.
        if len(self.obs.shape) == 2:
            return self.obs[end_idx-1]
        # if there weren't enough frames ever in the buffer for context
        if start_idx < 0 and self.num_in_buffer != self.size:
            start_idx = 0
        for idx in range(start_idx, end_idx - 1):
            if self.done[idx % self.size]:
                start_idx = idx + 1
        missing_context = self.frame_history_len - (end_idx - start_idx)
        # if zero padding is needed for missing context
        # or we are on the boundry of the buffer
        if start_idx < 0 or missing_context > 0:
            frames = [np.zeros_like(self.obs[0]) for _ in range(missing_context)]
            for idx in range(start_idx, end_idx):
                frames.append(self.obs[idx % self.size])
            return np.concatenate(frames, 2)
        else:
            # this optimization has potential to saves about 30% compute time \o/
            img_h, img_w = self.obs.shape[1], self.obs.shape[2]
            return self.obs[start_idx:end_idx].transpose(1, 2, 0, 3).reshape(img_h, img_w, -1)

def do_batch_sampling(pp_unl, batch_size):
    """
    return batch_size number of of item, sampled by uncertainty
    each with a label sampled from prob
    """
    n = pp_unl.shape[0]
    uncertain = np.abs(pp_unl[:,0] - 0.5)
    if n < batch_size:
        batch_size = n
    sam_weight = np.exp(20 * (1-uncertain))
    items = np.random.choice(n, batch_size, replace = False, p = sam_weight*1.0 / np.sum(sam_weight) )
    labels = np.zeros_like(items)

    for (i,item) in enumerate(items):
        if np.random.random() < pp_unl[item, 1]: 
            l = 1 
        else:
            l = 0
        labels[i] = l

    return (items, labels)

def viterbi_decode(score, transition_params):
  """ Adapted from Tensorflow implementation.
  Decode the highest scoring sequence of tags outside of TensorFlow.
  This should only be used at test time.
  Args:
    score: A [seq_len, num_tags] matrix of unary potentials.
    transition_params: A [num_tags, num_tags] matrix of binary potentials.
  Returns:
    viterbi: A [seq_len] list of integers containing the highest scoring tag
        indicies.
    viterbi_score: A float containing the score for the Viterbi sequence.
  """
  trellis = numpy.zeros_like(score)
  backpointers = numpy.zeros_like(score, dtype=numpy.int32)
  trellis[0] = score[0]
  for t in range(1, score.shape[0]):
    v = numpy.expand_dims(trellis[t - 1], 1) + transition_params
    trellis[t] = score[t] + numpy.max(v, 0)
    backpointers[t] = numpy.argmax(v, 0)
  viterbi = [numpy.argmax(trellis[-1])]
  for bp in reversed(backpointers[1:]):
    viterbi.append(bp[viterbi[-1]])
  viterbi.reverse()
  viterbi_score = numpy.max(trellis[-1])
  return viterbi, viterbi_score

def __init__(self, syn0, syn1):
        # Hyperparameters
        self.__b1 = 0.9
        self.__b2 = 0.999
        self.__learing_rate = 0.001
        self.__epsilon = 1e-8

        # initialize momentum
        self.__m_syn0 = np.zeros_like(syn0)
        self.__m_syn1 = np.zeros_like(syn1)
        self.__v_syn0 = np.zeros_like(syn0)
        self.__v_syn1 = np.zeros_like(syn1)

        # get a copy of weight
        self.__syn0 = copy.deepcopy(syn0)
        self.__syn1 = copy.deepcopy(syn1)

def __init__(self, image_a, image_b, window_size=32, search_size=32, distance=16):
        """
        Initialization of the class.

        :param image_a: first image to be evaluated
        :param image_b: second image to be evaluated
        :param int window_size: size of the interrogation window on first image
        :param int search_size: size of the search window on second image
        :param int distance: distance between beginning if first interrogation window and second
        """
        image_a, image_b = self._check_images(image_a, image_b)
        self.grid_spec = GridSpec(image_a.shape, image_a.strides,
                                   window_size, search_size, distance)

        self._correlator = FFTCorrelator(window_size, search_size)
        self._set_images(image_a, image_b)

        self.u = np.zeros(self.grid_spec.get_grid_shape())
        self.v = np.zeros_like(self.u)
        self._grid_creator()

def segmentation(_image, typeOfFruit):
    #_denoisedImage = rank.median(_image, disk(2));
    #_elevationMap = sobel(_denoisedImage);
    #print(_image);
    _elevationMap = sobel(_image);
    #print(_image);
    _marker = np.zeros_like(_image);
    if (typeOfFruit == 'Counting'):
        _marker[_image < 1998] = 1;
        _marker[_image > 61541] = 2;
    elif (typeOfFruit == 'Temperature'):
        #print("Printing Image");
        #print(_image < 10);
        _marker[_image < 30] = 1;
        #print(_marker);
        _marker[_image > 150] = 2;
    #_marker = rank.gradient(_denoisedImage, disk(5)) < 10;
    #_marker = ndi.label(_marker)[0];
    #_elevationMap = rank.gradient(_denoisedImage, disk(2));
    _segmentation = watershed(_elevationMap, _marker);
    #print(_segmentation);
    return _segmentation;

def segmentation(_image, typeOfFruit):
    #_denoisedImage = rank.median(_image, disk(2));
    #_elevationMap = sobel(_denoisedImage);
    #print(_image);
    _elevationMap = sobel(_image);
    #print(_image);
    _marker = np.zeros_like(_image);
    if (typeOfFruit == 'Counting'):
        _marker[_image < 1998] = 1;
        _marker[_image > 61541] = 2;
    elif (typeOfFruit == 'Temperature'):
        #print("Printing Image");        
        #print(_image < 10);
        _marker[_image < 30] = 1;
        #print(_marker);
        _marker[_image > 150] = 2;
    #_marker = rank.gradient(_denoisedImage, disk(5)) < 10;
    #_marker = ndi.label(_marker)[0];
    #_elevationMap = rank.gradient(_denoisedImage, disk(2));
    _segmentation = watershed(_elevationMap, _marker);
    #print(_segmentation);
    return _segmentation;

def dbFun(_x,_original_vals, f):
    db = DBSCAN(eps=0.3, min_samples=20).fit(_x)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True

    labels = db.labels_
    #print(labels)
    n_clusters_ = len(set(labels)) - (1 if -1 else 0)
    #gettingCharacteristics(_x, core_samples_mask, labels, n_clusters_,
    #_original_vals)
    print("Wait plotting clusters.....")
    plotCluster(_x, labels, core_samples_mask, n_clusters_, f)
    return

##############################################################################################
# Plotting the cluster after the result of DBSCAN

def overlap_ratio(boxes1, boxes2):
  # find intersection bbox
  x_int_bot = np.maximum(boxes1[:, 0], boxes2[0])
  x_int_top = np.minimum(boxes1[:, 0] + boxes1[:, 2], boxes2[0] + boxes2[2])
  y_int_bot = np.maximum(boxes1[:, 1], boxes2[1])
  y_int_top = np.minimum(boxes1[:, 1] + boxes1[:, 3], boxes2[1] + boxes2[3])

  # find intersection area
  dx = x_int_top - x_int_bot
  dy = y_int_top - y_int_bot
  area_int = np.where(np.logical_and(dx>0, dy>0), dx * dy, np.zeros_like(dx))

  # find union
  area_union = boxes1[:,2] * boxes1[:,3] + boxes2[2] * boxes2[3] - area_int

  # find overlap ratio
  ratio = np.where(area_union > 0, area_int/area_union, np.zeros_like(area_int))
  return ratio


###########################################################################
#                          overlap_ratio of two bboxes                    #
###########################################################################

def overlap_ratio_pair(boxes1, boxes2):
  # find intersection bbox
  x_int_bot = np.maximum(boxes1[:, 0], boxes2[:, 0])
  x_int_top = np.minimum(boxes1[:, 0] + boxes1[:, 2], boxes2[:, 0] + boxes2[:, 2])
  y_int_bot = np.maximum(boxes1[:, 1], boxes2[:, 1])
  y_int_top = np.minimum(boxes1[:, 1] + boxes1[:, 3], boxes2[:, 1] + boxes2[:, 3])

  # find intersection area
  dx = x_int_top - x_int_bot
  dy = y_int_top - y_int_bot
  area_int = np.where(np.logical_and(dx>0, dy>0), dx * dy, np.zeros_like(dx))

  # find union
  area_union = boxes1[:,2] * boxes1[:,3] + boxes2[:, 2] * boxes2[:, 3] - area_int

  # find overlap ratio
  ratio = np.where(area_union > 0, area_int/area_union, np.zeros_like(area_int))
  return ratio

def get_Conductivity(XYZ, sig0, sig1, R):
    """
    Define the conductivity for each point of the space
    """
    x, y, z = XYZ[:, 0], XYZ[:, 1], XYZ[:, 2]
    r_view = r(x, y, z)

    ind0 = (r_view > R)
    ind1 = (r_view <= R)

    assert (ind0 + ind1).all(), 'Some indicies not included'

    Sigma = np.zeros_like(x)

    Sigma[ind0] = sig0
    Sigma[ind1] = sig1

    return Sigma

def E_field_from_SheetCurruent(XYZ, srcLoc, sig, t, E0=1., orientation='X', kappa=0., epsr=1.):
    """
        Computing Analytic Electric fields from Plane wave in a Wholespace
        TODO:
            Add description of parameters
    """

    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
    # Check
    if XYZ.shape[0] > 1 & t.shape[0] > 1:
        raise Exception("I/O type error: For multiple field locations only a single frequency can be specified.")

    mu = mu_0*(1+kappa)

    if orientation == "X":
        z = XYZ[:, 2]
        bunja = -E0*(mu*sig)**0.5 * z * np.exp(-(mu*sig*z**2) / (4*t))
        bunmo = 2 * np.pi**0.5 * t**1.5
        Ex = bunja / bunmo
        Ey = np.zeros_like(z)
        Ez = np.zeros_like(z)
        return Ex, Ey, Ez
    else:
        raise NotImplementedError()

def H_field_from_SheetCurruent(XYZ, srcLoc, sig, t, E0=1., orientation='X', kappa=0., epsr=1.):
    """
        Plane wave propagating downward (negative z (depth))
    """

    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
    # Check
    if XYZ.shape[0] > 1 & t.shape[0] > 1:
        raise Exception("I/O type error: For multiple field locations only a single frequency can be specified.")

    mu = mu_0*(1+kappa)
    if orientation == "X":
        z = XYZ[:, 2]
        Hx = np.zeros_like(z)
        Hy = E0 * np.sqrt(sig / (np.pi*mu*t))*np.exp(-(mu*sig*z**2) / (4*t))
        Hz = np.zeros_like(z)
        return Hx, Hy, Hz
    else:
        raise NotImplementedError()

def appres(F, H, sig, chg, taux, c, mu, eps, n):

    Res = np.zeros_like(F)
    Phase = np.zeros_like(F)
    App_ImpZ= np.zeros_like(F, dtype='complex_')

    for i in range(0, len(F)):

        UD, EH, Z , K = Propagate(F[i], H, sig, chg, taux, c, mu, eps, n)

        App_ImpZ[i] = EH[0, 1]/EH[1, 1]

        Res[i] = np.abs(App_ImpZ[i])**2./(mu_0*omega(F[i]))
        Phase[i] = np.angle(App_ImpZ[i], deg = True)

    return Res, Phase

# Evaluate Up, Down components, E and H field, for a frequency range,
# a discretized depth range and a time range (use to calculate envelope)

def run(n, plotIt=True):
    # something to make a plot

    F = frange(-5., 5., 20)
    H = thick(50., 100., n)
    sign = sig(-5., 0., n)
    mun = mu(1., 2., n)
    epsn = eps(1., 9., n)
    chg = np.zeros_like(sign)
    taux = np.zeros_like(sign)
    c = np.zeros_like(sign)

    Res, Phase = appres(F, H, sign, chg, taux, c, mun, epsn, n)

    if plotIt:

        PlotAppRes(F, H, sign, chg, taux, c, mun, epsn, n, fenvelope=1000., PlotEnvelope=True)

    return Res, Phase

def J_field_from_SheetCurruent(XYZ, srcLoc, sig, f, E0=1., orientation='X', kappa=0., epsr=1., t=0.):
    """
        Plane wave propagating downward (negative z (depth))
    """

    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
    # Check
    if XYZ.shape[0] > 1 & f.shape[0] > 1:
        raise Exception("I/O type error: For multiple field locations only a single frequency can be specified.")

    mu = mu_0*(1+kappa)
    epsilon = epsilon_0*epsr
    sig_hat = sig + 1j*omega(f)*epsilon
    k  = np.sqrt( omega(f)**2. *mu*epsilon -1j*omega(f)*mu*sig )

    if orientation == "X":
        z = XYZ[:,2]
        Jx = sig*E0*np.exp(1j*(k*(z-srcLoc)+omega(f)*t))
        Jy = np.zeros_like(z)
        Jz = np.zeros_like(z)
        return Jx, Jy, Jz
    else:
        raise NotImplementedError()

def H_field_from_SheetCurruent(XYZ, srcLoc, sig, f, E0=1., orientation='X', kappa=0., epsr=1., t=0.):
    """
        Plane wave propagating downward (negative z (depth))
    """

    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
    # Check
    if XYZ.shape[0] > 1 & f.shape[0] > 1:
        raise Exception("I/O type error: For multiple field locations only a single frequency can be specified.")

    mu = mu_0*(1+kappa)
    epsilon = epsilon_0*epsr
    sig_hat = sig + 1j*omega(f)*epsilon
    k  = np.sqrt( omega(f)**2. *mu*epsilon -1j*omega(f)*mu*sig )
    Z = omega(f)*mu/k
    if orientation == "X":
        z = XYZ[:,2]
        Hx = np.zeros_like(z)
        Hy = E0/Z*np.exp(1j*(k*(z-srcLoc)+omega(f)*t))
        Hz = np.zeros_like(z)
        return Hx, Hy, Hz
    else:
        raise NotImplementedError()

def B_field_from_SheetCurruent(XYZ, srcLoc, sig, f, E0=1., orientation='X', kappa=0., epsr=1., t=0.):
    """
        Plane wave propagating downward (negative z (depth))
    """

    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
    # Check
    if XYZ.shape[0] > 1 & f.shape[0] > 1:
        raise Exception("I/O type error: For multiple field locations only a single frequency can be specified.")

    mu = mu_0*(1+kappa)
    epsilon = epsilon_0*epsr
    sig_hat = sig + 1j*omega(f)*epsilon
    k  = np.sqrt( omega(f)**2. *mu*epsilon -1j*omega(f)*mu*sig )
    Z = omega(f)*mu/k
    if orientation == "X":
        z = XYZ[:,2]
        Bx = mu*np.zeros_like(z)
        By = mu*E0/Z*np.exp(1j*(k*(z-srcLoc)+omega(f)*t))
        Bz = mu*np.zeros_like(z)
        return Bx, By, Bz
    else:
        raise NotImplementedError()

def stup():
    x0 = x[0]
    init_cond = np.zeros_like(X)
    for i in range(0, x_nods_quantity):
        if 0 <= x0 < 0.3:
            init_cond[i] = 0
            x0 += h
        elif(0.3 <= x0 <= 0.7):
            init_cond[i] = 1
            x0 += h
        else:
            init_cond[i] = 0
            x0 += h
    return init_cond
#stupenka end
#different initial conditions end

# different transfer velocity

def iou_loss_val(p, t):
    tp, tt = p.reshape((p.shape[0], 2, 2)), t.reshape((t.shape[0], 2, 2))
    overlaps = np.zeros_like(tp, dtype=np.float32)
    overlaps[:, 0, :] = np.maximum(tp[:, 0, :], tt[:, 0, :])
    overlaps[:, 1, :] = np.minimum(tp[:, 1, :], tt[:, 1, :])
    intersection = overlaps[:, 1, :] - overlaps[:, 0, :]
    bool_overlap = np.min(intersection, axis=1) > 0
    intersection = intersection[:, 0] * intersection[:, 1]
    intersection = np.maximum(intersection, 0.)
    # print "bool", bool_overlap
    # print "Int", intersection
    dims_p = tp[:, 1, :] - tp[:, 0, :]
    areas_p = dims_p[:, 0] * dims_p[:, 1]
    dims_t = tt[:, 1, :] - tt[:, 0, :]
    areas_t = dims_t[:, 0] * dims_t[:, 1]
    union = areas_p + areas_t - intersection
    # print "un", union
    loss = 1. - np.minimum(
        np.exp(np.log(np.abs(intersection)) - np.log(np.abs(union) + 1e-5)),
        1.
    )
    # print loss
    return np.mean(loss)

def test_constant_network_with_tags_dry_run(self):
    shape1 = loom.TypeShape('int64', (3,), 'alpha')
    shape2 = loom.TypeShape('int64', (3,), 'beta')
    value1 = np.array([1, 2, 3], dtype='int64')
    value2 = np.array([4, 5, 6], dtype='int64')
    ops = {'add1': BinaryLoomOp(shape1, tf.add),
           'add2': BinaryLoomOp(shape2, tf.add)}
    the_loom = loom.Loom(named_ops=ops, dry_run=True)
    output_tensor1 = the_loom.output_tensor(shape1)
    output_tensor2 = the_loom.output_tensor(shape2)
    with self.test_session():
      weaver = the_loom.make_weaver()
      c1 = weaver(value1, tag='alpha')
      c2 = weaver(value2, tag='beta')
      result1 = output_tensor1.eval(
          feed_dict=weaver.build_feed_dict([c2, c1]))
      result2 = output_tensor2.eval(
          feed_dict=weaver.build_feed_dict([c2, c1]))
    zero_vec = np.zeros_like(value1)
    self.assertTrue((result1[0] == zero_vec).all())
    self.assertTrue((result2[0] == zero_vec).all())

def optimise_f2_thresholds(y, p, verbose=True, resolution=100):
    def mf(x):
        p2 = np.zeros_like(p)
        for i in range(17):
            p2[:, i] = (p[:, i] > x[i]).astype(np.int)
        score = fbeta_score(y, p2, beta=2, average='samples')
        return score

    x = [0.2] * 17
    for i in range(17):
        best_i2 = 0
        best_score = 0
        for i2 in range(resolution):
            i2 /= resolution
            x[i] = i2
            score = mf(x)
            if score > best_score:
                best_i2 = i2
                best_score = score
        x[i] = best_i2
        if verbose:
            print(i, best_i2, best_score)

    return x

def buildFock(self):
        """Routine to build the AO basis Fock matrix"""
        if self.direct:
            if self.incFockRst: # restart incremental fock build?
                self.G = formPT(self.P,np.zeros_like(self.P),self.bfs,
                                self.nbasis,self.screen,self.scrTol)
                self.G = 0.5*(self.G + self.G.T) 
                self.F = self.Core.astype('complex') + self.G
            else:
                self.G = formPT(self.P,self.P_old,self.bfs,self.nbasis,
                                self.screen,self.scrTol)
                self.G = 0.5*(self.G + self.G.T) 
                self.F = self.F_old + self.G

        else:
            self.J = np.einsum('pqrs,sr->pq', self.TwoE.astype('complex'),self.P)
            self.K = np.einsum('psqr,sr->pq', self.TwoE.astype('complex'),self.P)
            self.G = 2.*self.J - self.K
            self.F = self.Core.astype('complex') + self.G

def svm_loss(x, y):
    """
    Computes the loss and gradient using for multiclass SVM classification.

    Inputs:
    - x: Input data, of shape (N, C) where x[i, j] is the score for the jth class
      for the ith input.
    - y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and
      0 <= y[i] < C

    Returns a tuple of:
    - loss: Scalar giving the loss
    - dx: Gradient of the loss with respect to x
    """
    N = x.shape[0]
    correct_class_scores = x[np.arange(N), y]
    margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)
    margins[np.arange(N), y] = 0
    loss = np.sum(margins) / N
    num_pos = np.sum(margins > 0, axis=1)
    dx = np.zeros_like(x)
    dx[margins > 0] = 1
    dx[np.arange(N), y] -= num_pos
    dx /= N
    return loss, dx

def sgd_momentum(w, dw, config=None):
    """
    Performs stochastic gradient descent with momentum.

    config format:
    - learning_rate: Scalar learning rate.
    - momentum: Scalar between 0 and 1 giving the momentum value.
      Setting momentum = 0 reduces to sgd.
    - velocity: A numpy array of the same shape as w and dw used to store a moving
      average of the gradients.

    """
    v = config.get('velocity', np.zeros_like(w))
    next_v = config['momentum'] * v - config['learning_rate'] * dw
    next_w = w + next_v
    config['velocity'] = next_v

    return next_w, config

def hls_select(image, thresh=(0, 255)):
    # 1) Convert to HLS color space
    hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
    H = hls[:, :, 0]
    L = hls[:, :, 1]
    S = hls[:, :, 2]
    # 2) Apply a threshold to the S channel
    thresh = (90, 255)
    binary = np.zeros_like(S)
    binary[(S > thresh[0]) & (S <= thresh[1])] = 1
    # 3) Return a binary image of threshold result
    return binary


# Define a function that applies Sobel x and y,
# then computes the direction of the gradient
# and applies a threshold.

def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):

    # Apply the following steps to img
    # 1) Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # 2) Take the gradient in x and y separately
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
    # 3) Take the absolute value of the x and y gradients
    abs_sobelx = np.absolute(sobelx)
    abs_sobely = np.absolute(sobely)
    # 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
    absgraddir = np.arctan2(abs_sobely, abs_sobelx)
    # 5) Create a binary mask where direction thresholds are met
    binary_output = np.zeros_like(absgraddir)
    binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
    # 6) Return this mask as your binary_output image
    return binary_output


# Define a function that applies Sobel x and y,
# then computes the magnitude of the gradient
# and applies a threshold

def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
    # Apply the following steps to img
    # 1) Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # 2) Take the gradient in x and y separately
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
    # 3) Calculate the magnitude
    gradmag = np.sqrt(sobelx**2 + sobely**2)
    # 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
    scale_factor = np.max(gradmag)/255
    gradmag = (gradmag/scale_factor).astype(np.uint8)
    # 5) Create a binary mask where mag thresholds are met
    binary_output = np.zeros_like(gradmag)
    binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
    # 6) Return this mask as your binary_output image
    return binary_output


# Define a function that applies Sobel x or y,
# then takes an absolute value and applies a threshold.
# Note: calling your function with orient='x', thresh_min=5, thresh_max=100
# should produce output like the example image shown above this quiz.

def abs_sobel_thresh(img, orient='x', thresh_min=0, thresh_max=255):

    # Apply the following steps to img
    # 1) Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # 2) Take the derivative in x or y given orient = 'x' or 'y'
    if orient == 'x':
        sobel = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
    if orient == 'y':
        sobel = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
    # 3) Take the absolute value of the derivative or gradient
    abs_sobel = np.absolute(sobel)
    # 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
    scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
    # 5) Create a mask of 1's where the scaled gradient magnitude
            # is > thresh_min and < thresh_max
    binary_output = np.zeros_like(scaled_sobel)
    binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
    # 6) Return this mask as your binary_output image
    return binary_output

def region_of_interest(img, vertices):
    """
    Applies an image mask.
    Only keeps the region of the image defined by the polygon
    formed from `vertices`. The rest of the image is set to black.
    """
    # defining a blank mask to start with
    mask = np.zeros_like(img)
    # defining a 3 channel or 1 channel color to fill the mask with depending on the input image
    if len(img.shape) > 2:
        channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image
        ignore_mask_color = (255,) * channel_count
    else:
        ignore_mask_color = 255
    # filling pixels inside the polygon defined by "vertices" with the fill color
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    # returning the image only where mask pixels are nonzero
    masked_image = cv2.bitwise_and(img, mask)
    return masked_image

def update_grad_input(self, x, grad_output, scale=1):
        x_cols = self.x_cols
        dout = grad_output
        N, C, H, W = self.x_shape
        pool_height, pool_width = self.kW, self.kH
        stride = self.dW
        pool_dim = pool_height * pool_width

        dout_reshaped = dout.transpose(2, 3, 0, 1).flatten()
        dx_cols = np.zeros_like(x_cols)
        dx_cols[:, np.arange(dx_cols.shape[1])] = 1. / pool_dim * dout_reshaped
        dx = col2im_cython(dx_cols, N * C, 1, H, W, pool_height, pool_width,
                           padding=0, stride=stride)

        self.grad_input = dx.reshape(self.x_shape)

        return self.grad_input

def update_grad_input(self, x, grad_output, scale=1):
        x_cols = self.x_cols
        x_cols_argmax = self.x_cols_argmax
        dout = grad_output
        N, C, H, W = x.shape
        pool_height, pool_width = self.kW, self.kH
        stride = self.dW

        dout_reshaped = dout.transpose(2, 3, 0, 1).flatten()
        dx_cols = np.zeros_like(x_cols)
        dx_cols[x_cols_argmax, np.arange(dx_cols.shape[1])] = dout_reshaped
        dx = col2im_cython(dx_cols, N * C, 1, H, W, pool_height, pool_width,
                           padding=0, stride=stride)
        dx = dx.reshape(self.x_shape)
        self.grad_input = dx
        return self.grad_input

def eval_numerical_gradient_array(f, x, df, h=1e-5):
    '''
    Evaluate a numeric gradient for a function that accepts a numpy
    array and returns a numpy array.
    '''
    grad = np.zeros_like(x)
    it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])
    while not it.finished:
        ix = it.multi_index

        oldval = x[ix]
        x[ix] = oldval + h
        pos = f(x).copy()
        x[ix] = oldval - h
        neg = f(x).copy()
        x[ix] = oldval

        grad[ix] = np.sum((pos - neg) * df) / (2 * h)
        it.iternext()
    return grad

def nesterov(w, dw, config=None):
    '''
    Performs stochastic gradient descent with nesterov momentum.

    config format:
    - learning_rate: Scalar learning rate.
    - momentum: Scalar between 0 and 1 giving the momentum value.
      Setting momentum = 0 reduces to sgd.
    - velocity: A numpy array of the same shape as w and dw used to store a moving
      average of the gradients.
    '''
    if config is None:
        config = {}
    config.setdefault('learning_rate', 1e-2)
    config.setdefault('momentum', 0.9)
    v = config.get('velocity', np.zeros_like(w, dtype=np.float64))

    next_w = None
    prev_v = v
    v = config['momentum'] * v - config['learning_rate'] * dw
    next_w = w - config['momentum'] * prev_v + (1 + config['momentum']) * v
    config['velocity'] = v

    return next_w, config

def sgd_momentum(w, dw, config=None):
    '''
    Performs stochastic gradient descent with momentum.

    config format:
    - learning_rate: Scalar learning rate.
    - momentum: Scalar between 0 and 1 giving the momentum value.
      Setting momentum = 0 reduces to sgd.
    - velocity: A numpy array of the same shape as w and dw used to store a moving
      average of the gradients.
    '''
    if config is None:
        config = {}
    config.setdefault('learning_rate', 1e-2)
    config.setdefault('momentum', 0.9)
    v = config.get('velocity', np.zeros_like(w))

    next_w = None
    v = config['momentum'] * v + config['learning_rate'] * dw
    next_w = w - v
    config['velocity'] = v

    return next_w, config

def test_float(self):
        # offset for alignment test
        for i in range(4):
            assert_array_equal(self.f[i:] > 0, self.ef[i:])
            assert_array_equal(self.f[i:] - 1 >= 0, self.ef[i:])
            assert_array_equal(self.f[i:] == 0, ~self.ef[i:])
            assert_array_equal(-self.f[i:] < 0, self.ef[i:])
            assert_array_equal(-self.f[i:] + 1 <= 0, self.ef[i:])
            r = self.f[i:] != 0
            assert_array_equal(r, self.ef[i:])
            r2 = self.f[i:] != np.zeros_like(self.f[i:])
            r3 = 0 != self.f[i:]
            assert_array_equal(r, r2)
            assert_array_equal(r, r3)
            # check bool == 0x1
            assert_array_equal(r.view(np.int8), r.astype(np.int8))
            assert_array_equal(r2.view(np.int8), r2.astype(np.int8))
            assert_array_equal(r3.view(np.int8), r3.astype(np.int8))

            # isnan on amd64 takes the same code path
            assert_array_equal(np.isnan(self.nf[i:]), self.ef[i:])

def test_double(self):
        # offset for alignment test
        for i in range(2):
            assert_array_equal(self.d[i:] > 0, self.ed[i:])
            assert_array_equal(self.d[i:] - 1 >= 0, self.ed[i:])
            assert_array_equal(self.d[i:] == 0, ~self.ed[i:])
            assert_array_equal(-self.d[i:] < 0, self.ed[i:])
            assert_array_equal(-self.d[i:] + 1 <= 0, self.ed[i:])
            r = self.d[i:] != 0
            assert_array_equal(r, self.ed[i:])
            r2 = self.d[i:] != np.zeros_like(self.d[i:])
            r3 = 0 != self.d[i:]
            assert_array_equal(r, r2)
            assert_array_equal(r, r3)
            # check bool == 0x1
            assert_array_equal(r.view(np.int8), r.astype(np.int8))
            assert_array_equal(r2.view(np.int8), r2.astype(np.int8))
            assert_array_equal(r3.view(np.int8), r3.astype(np.int8))

            # isnan on amd64 takes the same code path
            assert_array_equal(np.isnan(self.nd[i:]), self.ed[i:])

def test_round(self):
        def check_round(arr, expected, *round_args):
            assert_equal(arr.round(*round_args), expected)
            # With output array
            out = np.zeros_like(arr)
            res = arr.round(*round_args, out=out)
            assert_equal(out, expected)
            assert_equal(out, res)

        check_round(np.array([1.2, 1.5]), [1, 2])
        check_round(np.array(1.5), 2)
        check_round(np.array([12.2, 15.5]), [10, 20], -1)
        check_round(np.array([12.15, 15.51]), [12.2, 15.5], 1)
        # Complex rounding
        check_round(np.array([4.5 + 1.5j]), [4 + 2j])
        check_round(np.array([12.5 + 15.5j]), [10 + 20j], -1)

def test_basic(self):
        dts = [np.bool, np.int16, np.int32, np.int64, np.double, np.complex128,
               np.longdouble, np.clongdouble]
        for dt in dts:
            c = np.ones(53, dtype=np.bool)
            assert_equal(np.where( c, dt(0), dt(1)), dt(0))
            assert_equal(np.where(~c, dt(0), dt(1)), dt(1))
            assert_equal(np.where(True, dt(0), dt(1)), dt(0))
            assert_equal(np.where(False, dt(0), dt(1)), dt(1))
            d = np.ones_like(c).astype(dt)
            e = np.zeros_like(d)
            r = d.astype(dt)
            c[7] = False
            r[7] = e[7]
            assert_equal(np.where(c, e, e), e)
            assert_equal(np.where(c, d, e), r)
            assert_equal(np.where(c, d, e[0]), r)
            assert_equal(np.where(c, d[0], e), r)
            assert_equal(np.where(c[::2], d[::2], e[::2]), r[::2])
            assert_equal(np.where(c[1::2], d[1::2], e[1::2]), r[1::2])
            assert_equal(np.where(c[::3], d[::3], e[::3]), r[::3])
            assert_equal(np.where(c[1::3], d[1::3], e[1::3]), r[1::3])
            assert_equal(np.where(c[::-2], d[::-2], e[::-2]), r[::-2])
            assert_equal(np.where(c[::-3], d[::-3], e[::-3]), r[::-3])
            assert_equal(np.where(c[1::-3], d[1::-3], e[1::-3]), r[1::-3])