//  [python]
def __init__(self, rng, inputVar, cfgParams, copyLayer=None, layerNum=None):    
        """
        Allocate a LeNetConvPoolLayer with shared variable internal parameters.

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type inputVar: theano.tensor.dtensor4
        :param inputVar: symbolic image tensor, of shape image_shape

        :type cfgParams: ConvLayerParams
        """
                
        floatX = theano.config.floatX  # @UndefinedVariable
        
        self.cfgParams = cfgParams
        
        filter_shape = cfgParams.filter_shape
        image_shape = cfgParams.image_shape
        wInitOrthogonal = cfgParams.wInitOrthogonal

        assert image_shape[1] == filter_shape[1]
        self.inputVar = inputVar

        # there are "num inputVar feature maps * filter height * filter width"
        # inputs to each hidden unit
        fan_in = numpy.prod(filter_shape[1:])

        # each unit in the lower layer receives a gradient from:
        # "num output feature maps * filter height * filter width" /
        #   pooling size
        fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]))

        bInitVals = numpy.zeros((filter_shape[0],), dtype=floatX)
        
        # initialize weights with random weights
        if not (copyLayer is None):
            self.W = copyLayer.W
        else:
            if cfgParams.initValues is not None:
                if isinstance(cfgParams.initValues,basestring):
                    # load values from npz file
                        with open(cfgParams.initValues,"rb") as f:
                            vals = numpy.load(f)
                            wInitVals = vals['W'] 
                            bInitVals = vals['b']
                else:
                    if isinstance(cfgParams.initValues,dict):
                        wInitVals = cfgParams.initValues['W'] 
                        bInitVals = cfgParams.initValues['b']
                assert isinstance(wInitVals,numpy.ndarray), "Error loading W init values in HiddenLayer {}".format(self.cfgParams.name)
                assert isinstance(bInitVals,numpy.ndarray), "Error loading b init values in HiddenLayer {}".format(self.cfgParams.name)
            else:
                if cfgParams.wInitMode == 0:
                    W_bound = 1. / (fan_in + fan_out)
                    wInitVals = numpy.asarray(rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),dtype=floatX)
                elif cfgParams.wInitMode == 1:
                    W_bound = numpy.sqrt(1. / (fan_in + fan_out))
                    wInitVals = numpy.asarray(rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),dtype=floatX)
                elif cfgParams.wInitMode == 2:  # init
                    
                    wInitVals = numpy.zeros(filter_shape,dtype=floatX)
                    W_bound = numpy.sqrt(1. / (fan_in + fan_out))
                    #nFilters = cfgParams.nFilters
                    #nInputs = cfgParams.inputDim[1]
                    nFilters = filter_shape[0]
                    inputDim = filter_shape[1]
                    filterH = filter_shape[2]
                    filterW = filter_shape[3]
                    
                    filterSize = numpy.minimum(filterH,filterW)
                    mx,my = numpy.meshgrid(numpy.arange(filterH),numpy.arange(filterW),dtype=floatX)
                    
                    d = scipy.stats.norm(0,1)
                    y = d.pdf(numpy.linspace(-2,2,filterW))
                    y = y.reshape((filterW,1))
                    g = y.T*y
                    g = g/numpy.max(g)
                    
                    for i in range(nFilters):
                        for j in range(inputDim):
                            fx = numpy.random.rand()*2-1
                            fy = numpy.random.rand()*2-1
                            f = numpy.sin((mx*fx+my*fy)*2.*numpy.pi/filterSize)
                            fx = numpy.random.rand()*4-2
                            fy = numpy.random.rand()*4-2
                            f += numpy.random.rand() * numpy.sin((mx*fx-my*fy)*2.*numpy.pi/filterSize)
                            f = (f - numpy.min(f))*2*W_bound/(numpy.max(f)-numpy.min(f)) - W_bound                     
                            
                            f = f*g  # gaussian window
                            
                            wInitVals[i,j] = f
                
                
                # try pca of 
                # to create an orthogonal set of filters to start with
                #wInitOrthogonal = True # True ?
                if wInitOrthogonal:
                    wInitVals = numpy.reshape(wInitVals,(filter_shape[0],numpy.prod(filter_shape[1:])))
                    svd = numpy.linalg.svd(wInitVals.T)
                    U = svd[0]
                    wInitVals = U.T[0:filter_shape[0]].T
                    wInitVals = numpy.reshape(wInitVals.swapaxes(0,1), filter_shape)
            
            #wInitVals = numpy.asarray(numpy.ones(filter_shape,dtype=floatX)/(10*(fan_in+fan_out))) 
            self.W = theano.shared(wInitVals, borrow=True, name='convW{}'.format(layerNum))

        # the bias is a 1D tensor -- one bias per output feature map
        if not (copyLayer is None):
            self.b = copyLayer.b
        else:
            self.b = theano.shared(value=bInitVals, borrow=True, name='convB{}'.format(layerNum))

        # convolve inputVar feature maps with filters
        conv_out = conv.conv2d(input = inputVar, 
                               filters = self.W,
                               filter_shape = filter_shape, 
                               image_shape = image_shape)

        # add the bias term. Since the bias is a vector (1D array), we first
        # reshape it to a tensor of shape (1,n_filters,1,1). Each bias will
        # thus be broadcasted across mini-batches and feature map
        # width & height
        out = conv_out + self.b.dimshuffle('x', 0, 'x', 'x')
        
        self.output = out

        # store parameters of this layer
        self.params = [self.W, self.b]
        self.weights = [self.W]