python类vstack()的实例源码

def _block2df(block,obstypes,svnames,svnum):
    """
    input: block of text corresponding to one time increment INTERVAL of RINEX file
    output: 2-D array of float64 data from block. Future: consider whether best to use Numpy, Pandas, or Xray.
    """
    nobs = len(obstypes)
    stride=3

    strio = BytesIO(block.encode())
    barr = np.genfromtxt(strio, delimiter=(14,1,1)*5).reshape((svnum,-1), order='C')

    data = barr[:,0:nobs*stride:stride]
    lli  = barr[:,1:nobs*stride:stride]
    ssi  = barr[:,2:nobs*stride:stride]

    data = np.vstack(([data.T],[lli.T],[ssi.T])).T

    return data

def _block2df(block,obstypes,svnames,svnum):
    """
    input: block of text corresponding to one time increment INTERVAL of RINEX file
    output: 2-D array of float64 data from block.
    """
    nobs = len(obstypes)
    stride=3

    strio = BytesIO(block.encode())
    barr = np.genfromtxt(strio, delimiter=(14,1,1)*5).reshape((svnum,-1), order='C')

    data = barr[:,0:nobs*stride:stride]
    lli  = barr[:,1:nobs*stride:stride]
    ssi  = barr[:,2:nobs*stride:stride]

    data = np.vstack(([data],[lli],[ssi])).T #4D numpy array

    return data

def get_score_bounds_from_range(Z_min, Z_max, rho_lb, rho_ub, L0_max = None):
    "global variables: L0_reg_ind"
    edge_values = np.vstack([Z_min * rho_lb,
                             Z_max * rho_lb,
                             Z_min * rho_ub,
                             Z_max * rho_ub])

    if L0_max is None or L0_max == Z_min.shape[0]:
        s_min = np.sum(np.min(edge_values, axis = 0))
        s_max = np.sum(np.max(edge_values, axis = 0))
    else:
        min_values = np.min(edge_values, axis = 0)
        s_min_reg = np.sum(np.sort(min_values[L0_reg_ind])[0:L0_max])
        s_min_no_reg = np.sum(min_values[~L0_reg_ind])
        s_min = s_min_reg + s_min_no_reg

        max_values = np.max(edge_values, axis = 0)
        s_max_reg = np.sum(-np.sort(-max_values[L0_reg_ind])[0:L0_max])
        s_max_no_reg = np.sum(max_values[~L0_reg_ind])
        s_max = s_max_reg + s_max_no_reg

    return s_min, s_max


#setup weights

def get_score_bounds(Z_min, Z_max, rho_lb, rho_ub, L0_reg_ind = None, L0_max = None):
    edge_values = np.vstack([Z_min * rho_lb,
                             Z_max * rho_lb,
                             Z_min * rho_ub,
                             Z_max * rho_ub])

    if (L0_max is None) or (L0_reg_ind is None) or (L0_max == Z_min.shape[0]):
        s_min = np.sum(np.min(edge_values, axis=0))
        s_max = np.sum(np.max(edge_values, axis=0))
    else:
        min_values = np.min(edge_values, axis=0)
        s_min_reg = np.sum(np.sort(min_values[L0_reg_ind])[0:L0_max])
        s_min_no_reg = np.sum(min_values[~L0_reg_ind])
        s_min = s_min_reg + s_min_no_reg

        max_values = np.max(edge_values, axis=0)
        s_max_reg = np.sum(-np.sort(-max_values[L0_reg_ind])[0:L0_max])
        s_max_no_reg = np.sum(max_values[~L0_reg_ind])
        s_max = s_max_reg + s_max_no_reg

    return s_min, s_max

def _makeflat(self, start=None, end=None, groups = False):     
        eeg = list()
        for sub in self.data[start:end]:
            if len(sub) % self.chunk_len == 0:
                eeg.append(sub.reshape([-1, self.chunk_len,3]))
            else:
                print('ERROR: Please choose a chunk length that is a factor of {}. Current len = {}'.format(self.samples_per_epoch, len(sub)))
                return [0,0]
        hypno = list()
        group = list()
        hypno_repeat = self.samples_per_epoch / self.chunk_len
        idx = 0
        for sub in self.hypno[start:end]:
            hypno.append(np.repeat(sub, hypno_repeat))
            group.append(np.repeat(idx, len(hypno[-1])))
            idx += 1

        if groups:
            return np.vstack(eeg), np.hstack(hypno), np.hstack(group)
        else:
            return np.vstack(eeg), np.hstack(hypno)

def natural_key(string_):
    """See http://www.codinghorror.com/blog/archives/001018.html"""
    return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_)]   

#%%

#l=a['feat_eeg']
#val_acc = [y[0] for y in [x for x in l]]
#val_f1 = [y[1] for y in [x for x in l]]
#test_acc = [y[2] for y in [x for x in l]]
#test_f1 = [y[3] for y in [x for x in l]]
#
#val = np.vstack([val_acc, val_f1]).T
#test = np.vstack([test_acc, test_f1]).T

#a   = pickle.load(open('./results_dataset_feat_edfx','rb'))

def enroll(self, enroll_features):
    """enroll(enroll_features) -> model

    Enrolls the model by storing all given input vectors.

    **Parameters:**

    enroll_features : [1D :py:class:`numpy.ndarray`]
      The list of projected features to enroll the model from.

    **Returns:**

    model : 2D :py:class:`numpy.ndarray`
      The enrolled model.
    """
    assert len(enroll_features)
    [self._check_feature(feature, True) for feature in enroll_features]
    # just store all the features
    return numpy.vstack(enroll_features)

def enroll(self, enroll_features):
    """enroll(enroll_features) -> model

    Enrolls the model by storing all given input vectors.

    **Parameters:**

    ``enroll_features`` : [:py:class:`numpy.ndarray`]
      The list of projected features to enroll the model from.

    **Returns:**

    ``model`` : 2D :py:class:`numpy.ndarray`
      The enrolled model.
    """
    assert len(enroll_features)
    [self._check_feature(feature) for feature in enroll_features]
    # just store all the features
    return numpy.vstack(f.flatten() for f in enroll_features)

def train_projector(self, training_features, projector_file):
    """Generates the PCA covariance matrix and writes it into the given projector_file.

    **Parameters:**

    training_features : [1D :py:class:`numpy.ndarray`]
      A list of 1D training arrays (vectors) to train the PCA projection matrix with.

    projector_file : str
      A writable file, into which the PCA projection matrix (as a :py:class:`bob.learn.linear.Machine`) and the eigenvalues will be written.
    """
    # Assure that all data are 1D
    [self._check_feature(feature) for feature in training_features]

    # Initializes the data
    data = numpy.vstack(training_features)
    logger.info("  -> Training LinearMachine using PCA")
    t = bob.learn.linear.PCATrainer()
    self.machine, self.variances = t.train(data)
    # For re-shaping, we need to copy...
    self.variances = self.variances.copy()

    # compute variance percentage, if desired
    if isinstance(self.subspace_dim, float):
      cummulated = numpy.cumsum(self.variances) / numpy.sum(self.variances)
      for index in range(len(cummulated)):
        if cummulated[index] > self.subspace_dim:
          self.subspace_dim = index
          break
      self.subspace_dim = index
    logger.info("    ... Keeping %d PCA dimensions", self.subspace_dim)
    # re-shape machine
    self.machine.resize(self.machine.shape[0], self.subspace_dim)
    self.variances.resize(self.subspace_dim)

    f = bob.io.base.HDF5File(projector_file, "w")
    f.set("Eigenvalues", self.variances)
    f.create_group("Machine")
    f.cd("/Machine")
    self.machine.save(f)

def enroll(self, enroll_features):
    """enroll(enroll_features) -> model

    Enrolls the model by storing all given input vectors.

    **Parameters:**

    enroll_features : [1D :py:class:`numpy.ndarray`]
      The list of projected features to enroll the model from.

    **Returns:**

    model : 2D :py:class:`numpy.ndarray`
      The enrolled model.
    """
    assert len(enroll_features)
    [self._check_feature(feature, True) for feature in enroll_features]
    # just store all the features
    return numpy.vstack(enroll_features)

def _scores_d_normalize(t_model_ids, group):
  """Compute normalized D scores for the given T-model ids"""
  # the file selector object
  fs = FileSelector.instance()

  # initialize D and D_same_value matrices
  d_for_all = None
  d_same_value = None
  for t_model_id in t_model_ids:
    tmp = bob.io.base.load(fs.d_file(t_model_id, group))
    tmp2 = bob.io.base.load(fs.d_same_value_file(t_model_id, group))
    if d_for_all is None and d_same_value is None:
      d_for_all = tmp
      d_same_value = tmp2
    else:
      d_for_all = numpy.vstack((d_for_all, tmp))
      d_same_value = numpy.vstack((d_same_value, tmp2))

  # Saves to files
  bob.io.base.save(d_for_all, fs.d_matrix_file(group))
  bob.io.base.save(d_same_value, fs.d_same_value_matrix_file(group))

def x_frame2D(X, plot_limits=None, resolution=None):
    """
    Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
    """

    assert X.shape[1] == 2, \
        'x_frame2D is defined for two-dimensional inputs'
    if plot_limits is None:
        (xmin, xmax) = (X.min(0), X.max(0))
        (xmin, xmax) = (xmin - 0.2 * (xmax - xmin), xmax + 0.2 * (xmax
                        - xmin))
    elif len(plot_limits) == 2:
        (xmin, xmax) = plot_limits
    else:
        raise ValueError, 'Bad limits for plotting'

    resolution = resolution or 50
    (xx, yy) = np.mgrid[xmin[0]:xmax[0]:1j * resolution, xmin[1]:
                        xmax[1]:1j * resolution]
    Xnew = np.vstack((xx.flatten(), yy.flatten())).T
    return (Xnew, xx, yy, xmin, xmax)

def loadX(fname):
    '''
    Read data records into a data matrix.
    Also return vocabulary.
    '''

    events = []
    words_keys = set()
    for e in load(fname):
        events.append(e)
        words_keys = words_keys | set(e[5].keys())
    words_keys = sorted(list(words_keys))
    for (eidx, e) in enumerate(events):
        events[eidx] = list(e[:5]) + [e[5].get(word_key, 0)
                for word_key in words_keys]
    X = np.vstack(events)
    return (X, words_keys)

def get_document_batch(self, doc_id):
        """builds batch of all mention pairs in one document

        Args:
            doc_id: id of document

        Returns:
            feature representation of mentions and labels
        """
        mentions = self.dl.get_all_mentions_from_doc(doc_id)
        if len(mentions) == 0:
            return None, None
        A, B = [], []
        for a in mentions:
            for b in mentions:
                A.append(a)
                B.append(b)
        A_f = [self._mention_to_features(m) for m in A]
        B_f = [self._mention_to_features(m) for m in B]
        AB_f = self._pair_features(A, B)
        A = [self.dl.mention_features[m] for m in A]
        B = [self.dl.mention_features[m] for m in B]
        return np.vstack(A), np.stack(A_f), np.vstack(B), np.stack(B_f), np.stack(AB_f)

def log(p):
    q = p.rot
    t = p.trans
    r = quat.log(q)
    D = quat.dlog(r)
    return np.vstack((r, D * t))

def plotFields(layer,fieldShape=None,channel=None,figOffset=1,cmap=None,padding=0.01):
    # Receptive Fields Summary
    try:
        W = layer.W
    except:
        W = layer
    wp = W.eval().transpose();
    if len(np.shape(wp)) < 4:       # Fully connected layer, has no shape
        fields = np.reshape(wp,list(wp.shape[0:-1])+fieldShape) 
    else:           # Convolutional layer already has shape
        features, channels, iy, ix = np.shape(wp)
        if channel is not None:
            fields = wp[:,channel,:,:]
        else:
            fields = np.reshape(wp,[features*channels,iy,ix])

    perRow = int(math.floor(math.sqrt(fields.shape[0])))
    perColumn = int(math.ceil(fields.shape[0]/float(perRow)))

    fig = mpl.figure(figOffset); mpl.clf()

    # Using image grid
    from mpl_toolkits.axes_grid1 import ImageGrid
    grid = ImageGrid(fig,111,nrows_ncols=(perRow,perColumn),axes_pad=padding,cbar_mode='single')
    for i in range(0,np.shape(fields)[0]):
        im = grid[i].imshow(fields[i],cmap=cmap); 

    grid.cbar_axes[0].colorbar(im)
    mpl.title('%s Receptive Fields' % layer.name)

    # old way
    # fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
    # tiled = []
    # for i in range(0,perColumn*perRow,perColumn):
    #   tiled.append(np.hstack(fields2[i:i+perColumn]))
    # 
    # tiled = np.vstack(tiled)
    # mpl.figure(figOffset); mpl.clf(); mpl.imshow(tiled,cmap=cmap); mpl.title('%s Receptive Fields' % layer.name); mpl.colorbar();
    mpl.figure(figOffset+1); mpl.clf(); mpl.imshow(np.sum(np.abs(fields),0),cmap=cmap); mpl.title('%s Total Absolute Input Dependency' % layer.name); mpl.colorbar()

def plotOutput(layer,feed_dict,fieldShape=None,channel=None,figOffset=1,cmap=None):
    # Output summary
    try:
        W = layer.output
    except:
        W = layer
    wp = W.eval(feed_dict=feed_dict);
    if len(np.shape(wp)) < 4:       # Fully connected layer, has no shape
        temp = np.zeros(np.product(fieldShape)); temp[0:np.shape(wp.ravel())[0]] = wp.ravel()
        fields = np.reshape(temp,[1]+fieldShape)
    else:           # Convolutional layer already has shape
        wp = np.rollaxis(wp,3,0)
        features, channels, iy,ix = np.shape(wp)
        if channel is not None:
            fields = wp[:,channel,:,:]
        else:
            fields = np.reshape(wp,[features*channels,iy,ix])

    perRow = int(math.floor(math.sqrt(fields.shape[0])))
    perColumn = int(math.ceil(fields.shape[0]/float(perRow)))
    fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
    tiled = []
    for i in range(0,perColumn*perRow,perColumn):
        tiled.append(np.hstack(fields2[i:i+perColumn]))

    tiled = np.vstack(tiled)
    if figOffset is not None:
        mpl.figure(figOffset); mpl.clf(); 

    mpl.imshow(tiled,cmap=cmap); mpl.title('%s Output' % layer.name); mpl.colorbar();

def plotFields(layer,fieldShape=None,channel=None,maxFields=25,figName='ReceptiveFields',cmap=None,padding=0.01):
    # Receptive Fields Summary
    W = layer.W
    wp = W.eval().transpose();
    if len(np.shape(wp)) < 4:       # Fully connected layer, has no shape
        fields = np.reshape(wp,list(wp.shape[0:-1])+fieldShape)
    else:           # Convolutional layer already has shape
        features, channels, iy, ix = np.shape(wp)
        if channel is not None:
            fields = wp[:,channel,:,:]
        else:
            fields = np.reshape(wp,[features*channels,iy,ix])

    fieldsN = min(fields.shape[0],maxFields)
    perRow = int(math.floor(math.sqrt(fieldsN)))
    perColumn = int(math.ceil(fieldsN/float(perRow)))

    fig = mpl.figure(figName); mpl.clf()

    # Using image grid
    from mpl_toolkits.axes_grid1 import ImageGrid
    grid = ImageGrid(fig,111,nrows_ncols=(perRow,perColumn),axes_pad=padding,cbar_mode='single')
    for i in range(0,fieldsN):
        im = grid[i].imshow(fields[i],cmap=cmap);

    grid.cbar_axes[0].colorbar(im)
    mpl.title('%s Receptive Fields' % layer.name)

    # old way
    # fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
    # tiled = []
    # for i in range(0,perColumn*perRow,perColumn):
    #   tiled.append(np.hstack(fields2[i:i+perColumn]))
    #
    # tiled = np.vstack(tiled)
    # mpl.figure(figOffset); mpl.clf(); mpl.imshow(tiled,cmap=cmap); mpl.title('%s Receptive Fields' % layer.name); mpl.colorbar();
    mpl.figure(figName+' Total'); mpl.clf(); mpl.imshow(np.sum(np.abs(fields),0),cmap=cmap); mpl.title('%s Total Absolute Input Dependency' % layer.name); mpl.colorbar()

def plotOutput(layer,feed_dict,fieldShape=None,channel=None,figOffset=1,cmap=None):
    # Output summary
    W = layer.output
    wp = W.eval(feed_dict=feed_dict);
    if len(np.shape(wp)) < 4:       # Fully connected layer, has no shape
        temp = np.zeros(np.product(fieldShape)); temp[0:np.shape(wp.ravel())[0]] = wp.ravel()
        fields = np.reshape(temp,[1]+fieldShape)
    else:           # Convolutional layer already has shape
        wp = np.rollaxis(wp,3,0)
        features, channels, iy,ix = np.shape(wp)
        if channel is not None:
            fields = wp[:,channel,:,:]
        else:
            fields = np.reshape(wp,[features*channels,iy,ix])

    perRow = int(math.floor(math.sqrt(fields.shape[0])))
    perColumn = int(math.ceil(fields.shape[0]/float(perRow)))
    fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
    tiled = []
    for i in range(0,perColumn*perRow,perColumn):
        tiled.append(np.hstack(fields2[i:i+perColumn]))

    tiled = np.vstack(tiled)
    if figOffset is not None:
        mpl.figure(figOffset); mpl.clf();

    mpl.imshow(tiled,cmap=cmap); mpl.title('%s Output' % layer.name); mpl.colorbar();

def get_tm_opp(pts1, pts2):
    # Transformation matrix - ( Translation + Scaling + Rotation )
    # using Procuster analysis
    pts1 = np.float64(pts1)
    pts2 = np.float64(pts2)

    m1 = np.mean(pts1, axis = 0)
    m2 = np.mean(pts2, axis = 0)

    # Removing translation
    pts1 -= m1
    pts2 -= m2

    std1 = np.std(pts1)
    std2 = np.std(pts2)
    std_r = std2/std1

    # Removing scaling
    pts1 /= std1
    pts2 /= std2

    U, S, V = np.linalg.svd(np.transpose(pts1) * pts2)

    # Finding the rotation matrix
    R = np.transpose(U * V)

    return np.vstack([np.hstack((std_r * R,
        np.transpose(m2) - std_r * R * np.transpose(m1))), np.matrix([0.0, 0.0, 1.0])])