Python numpy.not_equal函数代码示例

def parseArgs(data, targetClass, otherClass = None, **args) :
    '''parse arguments for a feature scoring function'''

    if 'feature' in args :
        feature = args['feature']
    else :
        feature = None
    if 'Y' in args :
        Y = args['Y']
        if otherClass is None :
            otherI = numpy.nonzero(numpy.not_equal(Y, targetClass))[0]
        else :
            otherI = numpy.nonzero(numpy.equal(Y, otherClass))[0]
        targetClassSize = numpy.sum(numpy.equal(Y, targetClass))
    else :
        Y = None
        if otherClass is None :
            otherI = numpy.nonzero(numpy.not_equal(data.labels.Y, targetClass))[0]
        else :
            otherI = data.labels.classes[otherClass]
        targetClassSize = len(data.labels.classes[targetClass])
    
    otherClassSize = len(otherI)

    return Y, targetClassSize, otherClassSize, otherI, feature

    def node_can_drain(self, the_node):
        """Check if a node has drainage away from the current lake/depression.

        Parameters
        ----------
        the_node : int
            The node to test.
        nodes_this_depression : array_like of int
            Nodes that form a pit.

        Returns
        -------
        boolean
            ``True`` if the node can drain. Otherwise, ``False``.
        """
        nbrs = self._node_nbrs[the_node]
        not_bad = nbrs != LOCAL_BAD_INDEX_VALUE
        not_too_high = self._elev[nbrs] < self._elev[the_node]
        not_current_lake = np.not_equal(self.flood_status[nbrs], _CURRENT_LAKE)
        not_flooded = np.not_equal(self.flood_status[nbrs], _FLOODED)
        all_probs = np.logical_and(
            np.logical_and(not_bad, not_too_high),
            np.logical_and(not_current_lake, not_flooded))
        if np.any(all_probs):
            return True
        else:
            return False

def best_grid(wavelengths1, wavelengths2, key):
    """
    Return the best wavelength grid to regrid to arrays

    Considering the two wavelength grids passed in parameters, this function
    compute the best new grid that will be used to regrid the two spectra
    before combining them. We do not use np.unique as it is much slowe than
    finding the unique elements by hand.

    Parameters
    ----------
    wavelengths1, wavelengths2: array of floats
        The wavelength grids to be 'regridded'.
    key: tuple
        Key to key the results in cache.

    Returns
    -------
    new_grid: array of floats
        Array containing all the wavelengths found in the input arrays.

    """

    if key in best_grid_cache:
        return best_grid_cache[key]
    wl = np.concatenate((wavelengths1, wavelengths2))
    wl.sort(kind='mergesort')
    flag = np.ones(len(wl), dtype=bool)
    np.not_equal(wl[1:], wl[:-1], out=flag[1:])
    best_grid_cache[key] = wl[flag]
    return wl[flag]

    def compute_distances(self, x1, x2):
        """
        The method uses a function implemented in Cython. Data (`x1` and `x2`)
        is accompanied by two tables. One is a 2-d table in which elements of
        `x1` (`x2`) are replaced by 0's and 1's. The other is a vector
        indicating rows (or column) with nan values.

        The function in Cython uses a fast loop without any conditions to
        compute distances between rows without missing values, and a slower
        loop for those with missing values.
        """
        nonzeros1 = np.not_equal(x1, 0).view(np.int8)
        if self.axis == 1:
            nans1 = _distance.any_nan_row(x1)
            if x2 is None:
                nonzeros2, nans2 = nonzeros1, nans1
            else:
                nonzeros2 = np.not_equal(x2, 0).view(np.int8)
                nans2 = _distance.any_nan_row(x2)
            return _distance.jaccard_rows(
                nonzeros1, nonzeros2,
                x1, x1 if x2 is None else x2,
                nans1, nans2,
                self.ps,
                x2 is not None)
        else:
            nans1 = _distance.any_nan_row(x1.T)
            return _distance.jaccard_cols(
                nonzeros1, x1, nans1, self.ps)

def average_without_padding(x, ids, padding_id, cuda=False, eps=1e-8):
    if cuda:
        mask = Variable(torch.from_numpy(np.not_equal(ids, padding_id).astype(int)[:,:,np.newaxis])).float().cuda().permute(1, 2, 0).expand_as(x)
    else:
        mask = Variable(torch.from_numpy(np.not_equal(ids, padding_id).astype(int)[:,:,np.newaxis])).float().permute(1, 2, 0).expand_as(x)
    s = torch.sum(x*mask, dim=2) / (torch.sum(mask, dim=2)+eps)
    return s

def get_calipso_phase_inner(features, qual_min=CALIPSO_QUAL_VALUES['medium'],
                            max_layers=1, same_phase_in_top_three_lay=True):
    """
    Returns Calipso cloud phase.    
    Pixels with quality lower than *qual_min* are masked out.    
    Screen out pixels with more than *max_layers* layers.    
    """
    if same_phase_in_top_three_lay:
        phase1 = get_bits(features[:,0], CALIPSO_PHASE_BITS, shift=True)
        phase2 = get_bits(features[:,1], CALIPSO_PHASE_BITS, shift=True)
        phase3 = get_bits(features[:,2], CALIPSO_PHASE_BITS, shift=True)
        two_layer_pixels = features[:, 2] >1
        three_layer_pixels = features[:, 3] >1
        lay1_lay2_differ = np.logical_and(two_layer_pixels,
                                          np.not_equal(phase1, phase2))
        lay2_lay3_differ = np.logical_and(three_layer_pixels,
                                          np.not_equal(phase2, phase3))
        varying_phases_in_top_3lay = np.logical_or(lay1_lay2_differ,
                                                      lay2_lay3_differ)
    # Reduce to single layer, masking any multilayer pixels
    features = np.ma.array(features[:, 0],
                           mask=(features[:, max_layers:] > 1).any(axis=-1))
    if same_phase_in_top_three_lay:
        features = np.ma.array(features,                               
                                mask = varying_phases_in_top_3lay)
    phase = get_bits(features, CALIPSO_PHASE_BITS, shift=True)
    qual = get_bits(features, CALIPSO_QUAL_BITS, shift=True)    
    # Don't care about pixels with lower than *qual_min* quality
    return np.ma.array(phase, mask=qual < qual_min)

    def test_prelu_param_updates(self):
        x_train, _, y_train, _ = simple_classification()
        prelu_layer1 = layers.PRelu(20, alpha=0.25)
        prelu_layer2 = layers.PRelu(1, alpha=0.25)

        gdnet = algorithms.GradientDescent(
            [
                layers.Input(10),
                prelu_layer1,
                prelu_layer2,
            ]
        )

        prelu1_alpha_before_training = prelu_layer1.alpha.get_value()
        prelu2_alpha_before_training = prelu_layer2.alpha.get_value()

        gdnet.train(x_train, y_train, epochs=10)

        prelu1_alpha_after_training = prelu_layer1.alpha.get_value()
        prelu2_alpha_after_training = prelu_layer2.alpha.get_value()

        self.assertTrue(all(np.not_equal(
            prelu1_alpha_before_training,
            prelu1_alpha_after_training,
        )))
        self.assertTrue(all(np.not_equal(
            prelu2_alpha_before_training,
            prelu2_alpha_after_training,
        )))

def scoreDuplicates(records, data_model, pool, threshold=0):

    record, records = peek(records)

    id_type = idType(record)
    
    score_dtype = [('pairs', id_type, 2), ('score', 'f4', 1)]

    record_chunks = grouper(records, 100000)

    scoring_function = ScoringFunction(data_model, 
                                       threshold,
                                       score_dtype)

    results = [pool.apply_async(scoring_function,
                               (chunk,))
              for chunk in record_chunks] 

    for r in results :
       r.wait()

    scored_pairs = numpy.concatenate([r.get() for r in results])

    scored_pairs.sort()
    flag = numpy.ones(len(scored_pairs), dtype=bool)
    numpy.not_equal(scored_pairs[1:], 
                    scored_pairs[:-1], 
                    out=flag[1:])

    return scored_pairs[flag]

def merge(a, b):
    # http://stackoverflow.com/questions/12427146/combine-two-arrays-and-sort
    c = np.concatenate((a, b))
    c.sort(kind='mergesort')
    flag = np.ones(len(c), dtype=bool)
    np.not_equal(c[1:], c[:-1], out=flag[1:])

    return c[flag]

def _calc_errors(truth, prediction, class_number=1):
    tp = np.sum(np.equal(truth,class_number)*np.equal(prediction,class_number))
    tn = np.sum(np.not_equal(truth,class_number)*np.not_equal(prediction,class_number))

    fp = np.sum(np.not_equal(truth,class_number)*np.equal(prediction,class_number))
    fn = np.sum(np.equal(truth,class_number)*np.not_equal(prediction,class_number))

    return tp, tn, fp, fn

def oht_model( gw, oro, fsns, flns, shfl, lhfl ):
    """parameters; must be dimensioned as specified:
    gwi  : gaussian weights (lat)
    oroi : orography data array (lat,lon)
      requires the lat and lon are attached coordinates of oro 
      and that oro and the following variables are 2D arrays (lat,lon).
    fsnsi: net shortwave solar flux at surface (lat,lon)
    flnsi: net longwave solar flux at surface (lat,lon)
    shfli: sensible heat flux at surface  (lat,lon)
    lhfli: latent heat flux at surface  (lat,lon)
    """
    re = 6.371e6            # radius of earth
    coef = re**2/1.e15      # scaled by PW
    heat_storage = 0.3      # W/m^2 adjustment for ocean heat storage 

    nlat = oro.shape[0]
    nlon = oro.shape[1]
    dlon = 2.*pi/nlon       # dlon in radians
    lat = latAxis(oro)
    i65n = numpy.where( lat[:]>=65 )[0][0]   # assumes that lat[i+1]>lat[i]
    i65s = numpy.where( lat[:]<=-65 )[0][-1]  # assumes that lat[i+1]>lat[i]

    # get the mask for the ocean basins
    basins_mask = ocean_mask(oro)    # returns 2D array(lat,lon) 
    # compute net surface energy flux
    netflux = fsns-flns-shfl-lhfl-heat_storage

    # compute the net flux for the basins
    netflux_basin = numpy.ma.empty( (3,nlat,nlon) )
    netflux_basin[0,:,:] = netflux[:,:]
    netflux_basin[1,:,:] = netflux[:,:]
    netflux_basin[2,:,:] = netflux[:,:]
    netflux_basin[:,:,:] = numpy.ma.masked  # to make sure the mask array gets created
    netflux_basin._mask[0,:,:] = numpy.not_equal(basins_mask,1) # False on Pacific
    netflux_basin._mask[1,:,:] = numpy.not_equal(basins_mask,2) # False on Atlantic
    netflux_basin._mask[2,:,:] = numpy.not_equal(basins_mask,3) # False on Indian

    # sum flux over the longitudes in each basin
    heatflux = numpy.ma.sum( netflux_basin, axis=2 )

    # compute implied heat transport in each basin
    oft = cdms2.createVariable( numpy.ma.masked_all((4,nlat)) )
    oft.setAxisList( [cdms2.createAxis([0,1,2,3],id='basin numer'),lat] )
    # These ! signs assign a name to a dimension of oft:
    #oft!0 = "basin number"   # 0:pacific, 1:atlantic, 2:indian, 3:total
    #oft!1 = "lat"

    for n in range(3):
        for j in range(i65n,i65s-1,-1):      #start sum at most northern point
            # ...assumes that lat[i+1]>lat[i]
            oft[n,j] = -coef*dlon*numpy.ma.sum( heatflux[n,j:i65n+1]*gw[j:i65n+1] )

    # compute total implied ocean heat transport at each latitude
    # as the sum over the basins at that latitude
    for j in range( i65n, i65s-1, -1 ):
        oft[3,j] = numpy.ma.sum( oft[0:3,j] )

    return oft       # 2D array(4,lat)

def shrink_hyperrect(x0, x1, L, R):
    """
    
    """
    L_or_R = (x1 >= x0) #Modifications to R
    R[L_or_R] = x1[L_or_R]
    np.not_equal(L_or_R, True, L_or_R) #Modifications to L
    L[L_or_R] = x1[L_or_R]
    return L, R

    def _numpy(self, data, weights, shape):
        q = self.quantity(data)
        self._checkNPQuantity(q, shape)
        self._checkNPWeights(weights, shape)
        weights = self._makeNPWeights(weights, shape)
        newentries = weights.sum()

        import numpy

        selection = numpy.isnan(q)
        numpy.bitwise_not(selection, selection)
        subweights = weights.copy()
        subweights[selection] = 0.0
        self.nanflow._numpy(data, subweights, shape)

        # avoid nan warning in calculations by flinging the nans elsewhere
        numpy.bitwise_not(selection, selection)
        q = numpy.array(q, dtype=numpy.float64)
        q[selection] = self.high
        weights = weights.copy()
        weights[selection] = 0.0

        numpy.greater_equal(q, self.low, selection)
        subweights[:] = weights
        subweights[selection] = 0.0
        self.underflow._numpy(data, subweights, shape)

        numpy.less(q, self.high, selection)
        subweights[:] = weights
        subweights[selection] = 0.0
        self.overflow._numpy(data, subweights, shape)

        if all(isinstance(value, Count) and value.transform is identity for value in self.values) and numpy.all(numpy.isfinite(q)) and numpy.all(numpy.isfinite(weights)):
            # Numpy defines histograms as including the upper edge of the last bin only, so drop that
            weights[q == self.high] == 0.0

            h, _ = numpy.histogram(q, self.num, (self.low, self.high), weights=weights)

            for hi, value in zip(h, self.values):
                value.fill(None, float(hi))

        else:
            q = numpy.array(q, dtype=numpy.float64)
            numpy.subtract(q, self.low, q)
            numpy.multiply(q, self.num, q)
            numpy.divide(q, self.high - self.low, q)
            numpy.floor(q, q)
            q = numpy.array(q, dtype=int)

            for index, value in enumerate(self.values):
                numpy.not_equal(q, index, selection)
                subweights[:] = weights
                subweights[selection] = 0.0
                value._numpy(data, subweights, shape)

        # no possibility of exception from here on out (for rollback)
        self.entries += float(newentries)

    def _build_y(self, X, y, sample_weight, trim_duplicates=True):
        """Build the y_ IsotonicRegression."""
        check_consistent_length(X, y, sample_weight)
        X, y = [check_array(x, ensure_2d=False) for x in [X, y]]

        y = as_float_array(y)
        self._check_fit_data(X, y, sample_weight)

        # Determine increasing if auto-determination requested
        if self.increasing == 'auto':
            self.increasing_ = check_increasing(X, y)
        else:
            self.increasing_ = self.increasing

        # If sample_weights is passed, removed zero-weight values and clean
        # order
        if sample_weight is not None:
            sample_weight = check_array(sample_weight, ensure_2d=False)
            mask = sample_weight > 0
            X, y, sample_weight = X[mask], y[mask], sample_weight[mask]
        else:
            sample_weight = np.ones(len(y))

        order = np.lexsort((y, X))
        X, y, sample_weight = [astype(array[order], np.float64, copy=False)
                               for array in [X, y, sample_weight]]
        unique_X, unique_y, unique_sample_weight = _make_unique(
            X, y, sample_weight)

        # Store _X_ and _y_ to maintain backward compat during the deprecation
        # period of X_ and y_
        self._X_ = X = unique_X
        self._y_ = y = isotonic_regression(unique_y, unique_sample_weight,
                                           self.y_min, self.y_max,
                                           increasing=self.increasing_)

        # Handle the left and right bounds on X
        self.X_min_, self.X_max_ = np.min(X), np.max(X)

        if trim_duplicates:
            # Remove unnecessary points for faster prediction
            keep_data = np.ones((len(y),), dtype=bool)
            # Aside from the 1st and last point, remove points whose y values
            # are equal to both the point before and the point after it.
            keep_data[1:-1] = np.logical_or(
                np.not_equal(y[1:-1], y[:-2]),
                np.not_equal(y[1:-1], y[2:])
            )
            return X[keep_data], y[keep_data]
        else:
            # The ability to turn off trim_duplicates is only used to it make
            # easier to unit test that removing duplicates in y does not have
            # any impact the resulting interpolation function (besides
            # prediction speed).
            return X, y

def LabelPerimeter(L, Connectivity=4):
    """Converts a label or binary mask image to a binary perimeter image.

    Uses 4-neighbor or 8-neighbor shifts to detect pixels whose values do
    not agree with their neighbors.

    Parameters
    ----------
    L : array_like
        A label or binary mask image.
    Connectivity : double or int
        Neighborhood connectivity to evaluate. Valid values are 4 or 8.
        Default value = 4.

    Returns
    -------
    Mask : array_like
        A binary image where object perimeter pixels have value 1, and
        non-perimeter pixels have value 0.

    See Also
    --------
    EmbedBounds
    """

    # initialize temporary variable
    Mask = np.zeros(L.shape)
    Temp = np.zeros(L.shape)

    # check left-right neighbors
    Temp[:, 0:-2] = np.not_equal(L[:, 0:-2], L[:, 1:-1])
    Temp[:, 1:-1] = np.logical_or(Temp[:, 1:-1], Temp[:, 0:-2])
    Mask = np.logical_or(Mask, Temp)

    # check up-down neighbors
    Temp[0:-2, :] = np.not_equal(L[0:-2, :], L[1:-1, :])
    Temp[1:-1, :] = np.logical_or(Temp[1:-1, :], Temp[0:-2, :])
    Mask = np.logical_or(Mask, Temp)

    # additional calculations if Connectivity == 8
    if(Connectivity == 8):

        # slope 1 diagonal shift
        Temp[1:-1, 0:-2] = np.not_equal(L[0:-2, 1:-2], L[1:-1, 0:-2])
        Temp[0:-2, 1:-1] = np.logical_or(Temp[0:-2, 1:-1], Temp[1:-1, 0:-2])
        Mask = np.logical_or(Mask, Temp)

        # slope -1 diagonal shift
        Temp[1:-1, 1:-1] = np.not_equal(L[0:-2, 0:-2], L[1:-1, 1:-1])
        Temp[0:-2, 0:-2] = np.logical_or(Temp[0:-2, 0:-2], Temp[1:-1, 1:-1])
        Mask = np.logical_or(Mask, Temp)

    # generate label-valued output
    return Mask.astype(np.uint32) * L