Python sparse.csr_matrix函数代码示例

 def __init__(self, rng, P_input, L2_input, **kwargs):
     #symbol declaration, initialization and definition
     x_1_tm1, x_t = (\
             sparse.csr_matrix("x_1_tm1", dtype=theano.config.floatX),\
             sparse.csr_matrix("x_t",dtype=theano.config.floatX)\
         )\
         if P_input is None else P_input[:2]
     
     #elements of history
     shape = kwargs.get("shape")
     if shape is not None:
         dict_size = shape[0]
         if len(shape) <= 1:
             del shape["shape"]
         else:
             shape["shape"] = shape["shape"][1:]
     else:
         dict_size = (16,1,32,32)
     D_1_tm1 = theano.shared(rng.normal(size=dict_size).astype(theano.config.floatX))        
     Dx_1_tm1 = sparse.dot(x_1_tm1, D_1_tm1)#array access=dot operation      
     super(SequenceCNN, self).__init__(rng=rng, inputsymbol=Dx_1_tm1, **kwargs)#attaches new elements into the fgraph
     self.L2_output_1_tm1 = self.L2_output
     
     #elements of current time
     D_t = theano.shared(rng.normal(size=dict_size).astype(theano.config.floatX))        
     Dx_t = sparse.dot(x_t, D_t)#array access=dot operation
     self.L2_output_t = theano.clone(self.L2_output_1_tm1, replace={Dx_1_tm1:Dx_t})
     
     #element prepartion for model building
     self.P_input = (x_1_tm1,x_t)
     self.params += [D_1_tm1, D_t]
     self.L2_output = self.L2_output_1_tm1*self.L2_output_t

    def build(self):
        print "start building"
        x_sym = sparse.csr_matrix("x", dtype="float32")
        y_sym = T.imatrix("y")
        gx_sym_1 = sparse.csr_matrix("x", dtype="float32")
        gx_sym_2 = sparse.csr_matrix("x", dtype="float32")

        l_x_in = lasagne.layers.InputLayer(shape=(None, self.x.shape[1]), input_var=x_sym)
        l_hid = layers.SparseLayer(l_x_in, 50)
        embedding = lasagne.layers.get_output(l_hid)
        self.emb_fn = theano.function([x_sym], embedding)
        l_y = lasagne.layers.DenseLayer(l_hid, self.y.shape[1], nonlinearity=lasagne.nonlinearities.softmax)
        py_sym = lasagne.layers.get_output(l_y)
        loss = lasagne.objectives.categorical_crossentropy(py_sym, y_sym).mean()

        params = lasagne.layers.get_all_params(l_y, trainable=True)
        updates = lasagne.updates.sgd(loss, params, learning_rate=self.learning_rate)
        self.train_fn = theano.function([x_sym, y_sym], loss, updates=updates)

        l_gx_1 = lasagne.layers.InputLayer(shape=(None, self.x.shape[1]), input_var=gx_sym_1)
        l_gx_2 = lasagne.layers.InputLayer(shape=(None, self.x.shape[1]), input_var=gx_sym_2)
        l_gy_1 = layers.SparseLayer(l_gx_1, 50, W=l_hid.W, b=l_hid.b)
        l_gy_2 = layers.SparseLayer(l_gx_2, 50, W=l_hid.W, b=l_hid.b)
        gy_sym_1 = lasagne.layers.get_output(l_gy_1)
        gy_sym_2 = lasagne.layers.get_output(l_gy_2)
        g_loss = lasagne.objectives.squared_error(gy_sym_1, gy_sym_2).mean()

        g_params = lasagne.layers.get_all_params(l_gy_1) + lasagne.layers.get_all_params(l_gy_2)
        g_updates = lasagne.updates.sgd(g_loss, g_params, learning_rate=self.g_learning_rate)
        self.g_fn = theano.function([gx_sym_1, gx_sym_2], g_loss, updates=g_updates)

        acc = T.mean(T.eq(T.argmax(py_sym, axis=1), T.argmax(y_sym, axis=1)))
        self.test_fn = theano.function([x_sym, y_sym], acc)

        self.predict_fn = theano.function([x_sym], py_sym)

def create_TrainFunc_tranPES(simfn, embeddings,  marge=0.5, alpha=1., beta=1.):

    # parse the embedding data
    embedding = embeddings[0] # D x N matrix
    lembedding = embeddings[1]

    # declare the symbolic variables for training triples
    hp = S.csr_matrix('head positive') # N x batchsize matrix
    rp = S.csr_matrix('relation')
    tp = S.csr_matrix('tail positive')

    hn = S.csr_matrix('head negative')
    tn = S.csr_matrix('tail negative')

    lemb = T.scalar('embedding learning rate')
    lremb = T.scalar('relation learning rate')

    subtensorE = T.ivector('batch entities set')
    subtensorR = T.ivector('batch link set')

    # Generate the training positive and negative triples
    hpmat = S.dot(embedding.E, hp).T #  batchsize x D dense matrix
    rpmat = S.dot(lembedding.E, rp).T
    tpmat = S.dot(embedding.E, tp).T

    hnmat = S.dot(embedding.E, hn).T
    tnmat = S.dot(embedding.E, tn).T

    # calculate the score
    pos = tranPES3(simfn, T.concatenate([hpmat, tpmat], axis=1).reshape((hpmat.shape[0], 2, hpmat.shape[1])).dimshuffle(0, 2, 1), hpmat, rpmat, tpmat)


    negh = tranPES3(simfn, T.concatenate([hnmat, tpmat], axis=1).reshape((hnmat.shape[0], 2, hnmat.shape[1])).dimshuffle(0, 2, 1), hnmat, rpmat, tpmat)
    negt = tranPES3(simfn, T.concatenate([hpmat, tnmat], axis=1).reshape((hpmat.shape[0], 2, hpmat.shape[1])).dimshuffle(0, 2, 1), hpmat, rpmat, tnmat)

    costh, outh = margeCost(pos, negh, marge)
    costt, outt = margeCost(pos, negt, marge)

    embreg = regEmb(embedding, subtensorE, alpha)
    lembreg = regLink(lembedding, subtensorR, beta)
    

    cost = costh + costt + embreg[0] + lembreg
    out = T.concatenate([outh, outt])
    outc = embreg[1]

    # list of inputs to the function
    list_in = [lemb, lremb, hp, rp, tp, hn, tn, subtensorE, subtensorR]

    # updating the embeddings using gradient descend
    emb_grad = T.grad(cost, embedding.E)
    New_embedding = embedding.E - lemb*emb_grad

    remb_grad = T.grad(cost, lembedding.E)
    New_rembedding = lembedding.E - lremb * remb_grad

    updates = OrderedDict({embedding.E: New_embedding, lembedding.E: New_rembedding})

    return theano.function(list_in, [cost, T.mean(out), T.mean(outc), embreg[0], lembreg],
                          updates=updates, on_unused_input='ignore')

    def build(self):
        x_sym = sparse.csr_matrix("x", dtype="float32")
        self.x_sym = x_sym
        y_sym = T.imatrix("y")
        gx_sym = sparse.csr_matrix("gx", dtype="float32")
        gy_sym = T.ivector("gy")
        gz_sym = T.vector("gz")

        l_x_in = lasagne.layers.InputLayer(shape=(None, self.x.shape[1]), input_var=x_sym)
        l_gx_in = lasagne.layers.InputLayer(shape=(None, self.x.shape[1]), input_var=gx_sym)
        l_gy_in = lasagne.layers.InputLayer(shape=(None,), input_var=gy_sym)

        # l_x_1 = layers.SparseLayer(l_x_in, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)
        l_x_2 = layers.SparseLayer(l_x_in, self.embedding_size)
        W = l_x_2.W
        # l_x_2 = layers.DenseLayer(l_x_2, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)
        # l_x = lasagne.layers.ConcatLayer([l_x_1, l_x_2], axis = 1)
        # l_x = layers.DenseLayer(l_x, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)
        l_x = layers.DenseLayer(l_x_2, self.y.shape[1], nonlinearity=lasagne.nonlinearities.softmax)
        # l_x = layers.HybridLayer([l_x_in, l_x_2], self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)

        l_gx = layers.SparseLayer(l_gx_in, self.embedding_size, W=W)
        l_gy = lasagne.layers.EmbeddingLayer(l_gy_in, input_size=self.num_ver, output_size=self.embedding_size)
        l_gx = lasagne.layers.ElemwiseMergeLayer([l_gx, l_gy], T.mul)
        pgy_sym = lasagne.layers.get_output(l_gx)
        g_loss = -T.log(T.nnet.sigmoid(T.sum(pgy_sym, axis=1) * gz_sym)).sum()
        self.l = l_gx

        py_sym = lasagne.layers.get_output(l_x)
        self.ret_y = py_sym
        loss = lasagne.objectives.categorical_crossentropy(py_sym, y_sym).mean()
        # loss += lasagne.objectives.categorical_crossentropy(lasagne.layers.get_output(l_x_1), y_sym).mean()
        # loss += lasagne.objectives.categorical_crossentropy(lasagne.layers.get_output(l_x_2), y_sym).mean()

        # params = lasagne.layers.get_all_params(l_x)
        # params = [l_x_1.W, l_x_1.b, l_x_2.W, l_x_2.b, l_x.W, l_x.b]
        if not JOINT:
            params = [l_x.W, l_x.b]
        else:
            params = lasagne.layers.get_all_params(l_x)
        # params = [l_x.W1, l_x.W2, l_x.b]
        updates = lasagne.updates.sgd(loss, params, learning_rate=self.learning_rate)
        self.train_fn = theano.function([x_sym, y_sym], loss, updates=updates)

        g_params = lasagne.layers.get_all_params(l_gx)
        g_updates = lasagne.updates.sgd(g_loss, g_params, learning_rate=self.g_learning_rate)
        self.g_fn = theano.function([gx_sym, gy_sym, gz_sym], g_loss, updates=g_updates)

        acc = T.mean(T.eq(T.argmax(py_sym, axis=1), T.argmax(y_sym, axis=1)))
        self.test_fn = theano.function([x_sym, y_sym], acc)

def SimFn(fnsim, embeddings, leftop, rightop):
    """
    This function returns a Theano function to measure the similarity score
    for sparse matrices inputs.

    :param fnsim: similarity function (on Theano variables).
    :param embeddings: an Embeddings instance.
    :param leftop: class for the 'left' operator.
    :param rightop: class for the 'right' operator.
    """
    embedding, relationl, relationr = parse_embeddings(embeddings)

    # Inputs
    inpr = S.csr_matrix('inpr')
    inpl = S.csr_matrix('inpl')
    inpo = S.csr_matrix('inpo')
    # Graph
    #what is T? Are they tensor? lhs, rhs,rell,relr 
    # we just created inpl and inplr inpo . what does it mean to calculate dot product?
    lhs = S.dot(embedding.E, inpl).T
    rhs = S.dot(embedding.E, inpr).T
    rell = S.dot(relationl.E, inpo).T
    relr = S.dot(relationr.E, inpo).T
    
    # what is this?
    #ref:
    #leftop = LayerMat('lin', state.ndim, state.nhid)
    #rightop = LayerMat('lin', state.ndim, state.nhid)
    # on call 
    #ry = y.reshape((y.shape[0], self.n_inp, self.n_out))
    #rx = x.reshape((x.shape[0], x.shape[1], 1))
    #return self.act((rx * ry).sum(1))
    
    simi = fnsim(leftop(lhs, rell), rightop(rhs, relr))
    """
    Theano function inputs.
    :input inpl: sparse csr matrix (representing the indexes of the 'left'
                    entities), shape=(#examples, N [Embeddings]).
    :input inpr: sparse csr matrix (representing the indexes of the 'right'
                    entities), shape=(#examples, N [Embeddings]).
    :input inpo: sparse csr matrix (representing the indexes of the
                    relation member), shape=(#examples, N [Embeddings]).

    Theano function output
    :output simi: matrix of score values.
    """
    return theano.function([inpl, inpr, inpo], [simi],
            on_unused_input='ignore')

    def _setup_vars(self, sparse_input):
        '''Setup Theano variables for our network.

        Parameters
        ----------
        sparse_input : bool
            If True, create an input variable that can hold a sparse matrix.
            Defaults to False, which assumes all arrays are dense.

        Returns
        -------
        vars : list of theano variables
            A list of the variables that this network requires as inputs.
        '''
        # x represents our network's input.
        self.x = TT.matrix('x')
        if sparse_input:
            self.x = SS.csr_matrix('x')

        # for a classifier, this specifies the correct labels for a given input.
        self.labels = TT.ivector('labels')

        # and the weights are reshaped to be just a vector.
        self.weights = TT.vector('weights')

        if self.weighted:
            return [self.x, self.labels, self.weights]
        return [self.x, self.labels]

    def build(self):
        """build the model. This method should be called after self.add_data.
        """
        x_sym = sparse.csr_matrix('x', dtype = 'float32')
        y_sym = T.imatrix('y')
        g_sym = T.imatrix('g')
        gy_sym = T.vector('gy')
        ind_sym = T.ivector('ind')

        l_x_in = lasagne.layers.InputLayer(shape = (None, self.x.shape[1]), input_var = x_sym)
        l_g_in = lasagne.layers.InputLayer(shape = (None, 2), input_var = g_sym)
        l_ind_in = lasagne.layers.InputLayer(shape = (None, ), input_var = ind_sym)
        l_gy_in = lasagne.layers.InputLayer(shape = (None, ), input_var = gy_sym)

        num_ver = max(self.graph.keys()) + 1
        l_emb_in = lasagne.layers.SliceLayer(l_g_in, indices = 0, axis = 1)
        l_emb_in = lasagne.layers.EmbeddingLayer(l_emb_in, input_size = num_ver, output_size = self.embedding_size)
        l_emb_out = lasagne.layers.SliceLayer(l_g_in, indices = 1, axis = 1)
        if self.neg_samp > 0:
            l_emb_out = lasagne.layers.EmbeddingLayer(l_emb_out, input_size = num_ver, output_size = self.embedding_size)

        l_emd_f = lasagne.layers.EmbeddingLayer(l_ind_in, input_size = num_ver, output_size = self.embedding_size, W = l_emb_in.W)
        l_x_hid = layers.SparseLayer(l_x_in, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)
        
        if self.use_feature:
            l_emd_f = layers.DenseLayer(l_emd_f, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)
            l_y = lasagne.layers.ConcatLayer([l_x_hid, l_emd_f], axis = 1)
            l_y = layers.DenseLayer(l_y, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)
        else:
            l_y = layers.DenseLayer(l_emd_f, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)

        py_sym = lasagne.layers.get_output(l_y)
        loss = lasagne.objectives.categorical_crossentropy(py_sym, y_sym).mean()
        if self.layer_loss and self.use_feature:
            hid_sym = lasagne.layers.get_output(l_x_hid)
            loss += lasagne.objectives.categorical_crossentropy(hid_sym, y_sym).mean()
            emd_sym = lasagne.layers.get_output(l_emd_f)
            loss += lasagne.objectives.categorical_crossentropy(emd_sym, y_sym).mean()

        if self.neg_samp == 0:
            l_gy = layers.DenseLayer(l_emb_in, num_ver, nonlinearity = lasagne.nonlinearities.softmax)
            pgy_sym = lasagne.layers.get_output(l_gy)
            g_loss = lasagne.objectives.categorical_crossentropy(pgy_sym, lasagne.layers.get_output(l_emb_out)).sum()
        else:
            l_gy = lasagne.layers.ElemwiseMergeLayer([l_emb_in, l_emb_out], T.mul)
            pgy_sym = lasagne.layers.get_output(l_gy)
            g_loss = - T.log(T.nnet.sigmoid(T.sum(pgy_sym, axis = 1) * gy_sym)).sum()

        params = [l_emd_f.W, l_emd_f.b, l_x_hid.W, l_x_hid.b, l_y.W, l_y.b] if self.use_feature else [l_y.W, l_y.b]
        if self.update_emb:
            params = lasagne.layers.get_all_params(l_y)
        updates = lasagne.updates.sgd(loss, params, learning_rate = self.learning_rate)

        self.train_fn = theano.function([x_sym, y_sym, ind_sym], loss, updates = updates, on_unused_input = 'ignore')
        self.test_fn = theano.function([x_sym, ind_sym], py_sym, on_unused_input = 'ignore')
        self.l = [l_gy, l_y]

        g_params = lasagne.layers.get_all_params(l_gy, trainable = True)
        g_updates = lasagne.updates.sgd(g_loss, g_params, learning_rate = self.g_learning_rate)
        self.g_fn = theano.function([g_sym, gy_sym], g_loss, updates = g_updates, on_unused_input = 'ignore')

	def get_train_function(self):
		# specify the computational graph
		target = T.matrix('target')
		weight = theano.shared(np.random.randn(len(self.feature_map), len(self.label_map)), name='weight')
		feat_mat = sparse.csr_matrix(name='feat_mat')
		mask_mat = sparse.csr_matrix(name='mask_mat')
		sum_pred = sparse.dot( mask_mat, T.nnet.softmax( sparse.dot(feat_mat, weight) ) )
		pred = sum_pred / sum_pred.sum(axis=1).reshape((sum_pred.shape[0], 1))
		objective = T.nnet.categorical_crossentropy(pred, target).sum() + self.param.l2_regularization * (weight ** 2).sum()
		grad_weight = T.grad(objective, weight)

		# print 'Compiling function ...'
		# compile the function
		train = theano.function(inputs = [feat_mat, mask_mat, target], outputs = [objective, weight], updates=[(weight, weight - 0.1*grad_weight)] )

		return train

 def __init__(self, size, name="in", ndim=2, sparse=False):
     self.input = util.FLOAT_CONTAINERS[ndim](name)
     if sparse is True or isinstance(sparse, str) and sparse.lower() == "csr":
         assert ndim == 2, "Theano only supports sparse arrays with 2 dims"
         self.input = SS.csr_matrix("input")
     if isinstance(sparse, str) and sparse.lower() == "csc":
         assert ndim == 2, "Theano only supports sparse arrays with 2 dims"
         self.input = SS.csc_matrix("input")
     super(Input, self).__init__(size=size, name=name, inputs=0, activation="linear", ndim=ndim, sparse=sparse)

    def __init__(self, sparse_coding, nb_negative, embed_dims=128, context_dims=128,
                 init_embeddings=None, negprob_table=None, optimizer='adam'):
        super(NCELangModelV4, self).__init__(weighted_inputs=False)
        vocab_size = sparse_coding.shape[0]  # the extra word is for OOV
        self.nb_base = sparse_coding.shape[1] - 1
        self.vocab_size = vocab_size
        self.embed_dim = embed_dims
        self.optimizer = optimizers.get(optimizer)
        self.nb_negative = nb_negative
        self.loss = categorical_crossentropy
        self.loss_fnc = objective_fnc(self.loss)
        self.sparse_coding = sparse_coding

        if negprob_table is None:
            negprob_table_ = np.ones(shape=(vocab_size,), dtype=theano.config.floatX)/vocab_size
            negprob_table = theano.shared(negprob_table_)
            self.neg_prob_table = negprob_table_
        else:
            self.neg_prob_table = negprob_table.astype(theano.config.floatX)
            negprob_table = theano.shared(negprob_table.astype(theano.config.floatX))

        self.sampler = TableSampler(self.neg_prob_table)

        self.add_input(name='idxes', ndim=3, dtype='int32')
        idxes = self.inputs['idxes'].get_output(True)
        shape = idxes.shape[1:]
        codes = tsp.csr_matrix('sp-codes', dtype=floatX)
        nb_pos_words = shape[0] * shape[1]
        pos_codes = codes[:nb_pos_words]

        self.add_node(Identity(inputs={True: pos_codes, False: pos_codes}), name='codes_flat')
        self.add_node(Identity(inputs={True: shape, False: shape}), name='sents_shape')
        self.add_node(Identity(inputs={True: codes, False: codes}), name='sparse_codes')

        self.add_node(SparseEmbedding(self.nb_base+1, embed_dims, weights=init_embeddings),
                      name='embedding', inputs=('codes_flat', 'sents_shape'))
        self.add_node(LangLSTMLayer(embed_dims, output_dim=context_dims), name='encoder', inputs='embedding')
        # seq.add(Dropout(0.5))
        self.add_node(PartialSoftmaxV4(input_dim=context_dims, base_size=self.nb_base+1),
                      name='part_prob', inputs=('idxes', 'sparse_codes', 'encoder'))
        self.add_node(Dense(input_dim=context_dims, output_dim=1, activation='exponential'),
                      name='normalizer', inputs='encoder')
        self.add_node(LookupProb(negprob_table), name='lookup_prob', inputs='idxes')

        self.add_node(SharedWeightsDense(self.nodes['part_prob'].W, self.nodes['part_prob'].b, self.sparse_coding,
                                         activation='exponential'),
                      name='true_unnorm_prob', inputs='encoder')
        self.add_node(ActivationLayer(name='normalization'), name='true_prob', inputs='true_unnorm_prob')

        self.add_output('pos_prob', node='part_prob')
        self.add_output('neg_prob', node='lookup_prob')
        self.add_output('pred_prob', node='true_prob')
        self.add_output('normalizer', node='normalizer')
        self.add_output('unrm_prob', node='true_unnorm_prob')

 def __init__(self, size, name='in', ndim=2, sparse=False, **kwargs):
     self.input = util.FLOAT_CONTAINERS[ndim](name)
     if sparse is True or \
        isinstance(sparse, util.basestring) and sparse.lower() == 'csr':
         assert ndim == 2, 'Theano only supports sparse arrays with 2 dims'
         self.input = SS.csr_matrix('input')
     if isinstance(sparse, util.basestring) and sparse.lower() == 'csc':
         assert ndim == 2, 'Theano only supports sparse arrays with 2 dims'
         self.input = SS.csc_matrix('input')
     super(Input, self).__init__(
         size=size, name=name, activation='linear', ndim=ndim, sparse=sparse)

    def build(self):
        x_sym = sparse.csr_matrix('x', dtype = 'float32')
        self.x_sym = x_sym
        y_sym = T.imatrix('y')
        gx_sym = sparse.csr_matrix('gx', dtype = 'float32')
        gy_sym = T.ivector('gy')

        l_x_in = lasagne.layers.InputLayer(shape = (None, self.x.shape[1]), input_var = x_sym)
        l_gx_in = lasagne.layers.InputLayer(shape = (None, self.x.shape[1]), input_var = gx_sym)

        l_x_1 = layers.SparseLayer(l_x_in, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)
        l_x_2 = layers.SparseLayer(l_x_in, self.embedding_size)
        W = l_x_2.W
        embedding = lasagne.layers.get_output(l_x_2)
        self.emb_fn = theano.function([x_sym], embedding)
        l_x_2 = lasagne.layers.DenseLayer(l_x_2, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)
        l_x = lasagne.layers.ConcatLayer([l_x_1, l_x_2], axis = 1)
        l_x = lasagne.layers.DenseLayer(l_x, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)

        self.num_ver = max(self.graph.keys()) + 1
        l_gx = layers.SparseLayer(l_gx_in, self.embedding_size, W = W)
        l_gx = lasagne.layers.DenseLayer(l_gx, self.num_ver, nonlinearity = lasagne.nonlinearities.softmax)

        py_sym = lasagne.layers.get_output(l_x)
        self.ret_y = py_sym
        loss = lasagne.objectives.categorical_crossentropy(py_sym, y_sym).mean()

        pgy_sym = lasagne.layers.get_output(l_gx)
        g_loss = lasagne.objectives.categorical_crossentropy(pgy_sym, gy_sym).sum()

        # params = lasagne.layers.get_all_params(l_x)
        params = [l_x_1.W, l_x_1.b, l_x_2.W, l_x_2.b, l_x.W, l_x.b]
        updates = lasagne.updates.sgd(loss, params, learning_rate = self.learning_rate)
        self.train_fn = theano.function([x_sym, y_sym], loss, updates = updates)

        g_params = lasagne.layers.get_all_params(l_gx)
        g_updates = lasagne.updates.sgd(g_loss, g_params, learning_rate = self.g_learning_rate)
        self.g_fn = theano.function([gx_sym, gy_sym], g_loss, updates = g_updates)

        acc = T.mean(T.eq(T.argmax(py_sym, axis = 1), T.argmax(y_sym, axis = 1)))
        self.test_fn = theano.function([x_sym, y_sym], acc)

    def test_sparse(self):
        mySymbolicSparseList = TypedListType(sparse.SparseType("csr", theano.config.floatX))()
        mySymbolicSparse = sparse.csr_matrix()

        z = Count()(mySymbolicSparseList, mySymbolicSparse)

        f = theano.function([mySymbolicSparseList, mySymbolicSparse], z)

        x = sp.csr_matrix(random_lil((10, 40), theano.config.floatX, 3))
        y = sp.csr_matrix(random_lil((10, 40), theano.config.floatX, 3))

        self.assertTrue(f([x, y, y], y) == 2)

def ForwardFn(fnsim, embeddings, leftop, rightop, marge=1.0):
    """
    This function returns a theano function to perform a forward step,
    contrasting couples of positive and negative triplets. members are given
    as sparse matrices. For one positive triplet there is one negative
    triplet.

    :param fnsim: similarity function (on theano variables).
    :param embeddings: an embeddings instance.
    :param leftop: class for the 'left' operator.
    :param rightop: class for the 'right' operator.
    :param marge: marge for the cost function.

    :note: this is useful for W_SABIE [Weston et al., IJCAI 2011]
    """
    embedding, relationl, relationr = parse_embeddings(embeddings)

    # inputs
    inpr = S.csr_matrix()
    inpl = S.csr_matrix()
    inpo = S.csr_matrix()
    inpln = S.csr_matrix()
    inprn = S.csr_matrix()
    inpon = S.csr_matrix()

    # graph
    lhs = S.dot(embedding.E, inpl).T
    rhs = S.dot(embedding.E, inpr).T
    rell = S.dot(relationl.E, inpo).T
    relr = S.dot(relationr.E, inpo).T
    lhsn = S.dot(embedding.E, inpln).T
    rhsn = S.dot(embedding.E, inprn).T
    relln = S.dot(relationl.E, inpon).T
    relrn = S.dot(relationr.E, inpon).T
    simi = fnsim(leftop(lhs, rell), rightop(rhs, relr))
    simin = fnsim(leftop(lhsn, relln), rightop(rhsn, relrn))
    cost, out = margincost(simi, simin, marge)
    """
    Theano function inputs.
    :input inpl: sparse csr matrix representing the indexes of the positive
                 triplet 'left' member, shape=(#examples,N [Embeddings]).
    :input inpr: sparse csr matrix representing the indexes of the positive
                 triplet 'right' member, shape=(#examples,N [Embeddings]).
    :input inpo: sparse csr matrix representing the indexes of the positive
                 triplet relation member, shape=(#examples,N [Embeddings]).
    :input inpln: sparse csr matrix representing the indexes of the negative
                  triplet 'left' member, shape=(#examples,N [Embeddings]).
    :input inprn: sparse csr matrix representing the indexes of the negative
                  triplet 'right' member, shape=(#examples,N [Embeddings]).
    :input inpon: sparse csr matrix representing the indexes of the negative
                  triplet relation member, shape=(#examples,N [Embeddings]).

    Theano function output.
    :output out: binary vector representing when the margin is violated, i.e.
                 when an update occurs.
    """
    return theano.function([inpl, inpr, inpo,
                           inpln, inprn, inpon], [out],
                           on_unused_input='ignore')

    def test_sparse(self):
        if not scipy_imported:
            raise SkipTest("Optional package SciPy not installed")
        mySymbolicSparseList = TypedListType(sparse.SparseType("csr", theano.config.floatX))()
        mySymbolicSparse = sparse.csr_matrix()

        z = Count()(mySymbolicSparseList, mySymbolicSparse)

        f = theano.function([mySymbolicSparseList, mySymbolicSparse], z)

        x = sp.csr_matrix(random_lil((10, 40), theano.config.floatX, 3))
        y = sp.csr_matrix(random_lil((10, 40), theano.config.floatX, 3))

        self.assertTrue(f([x, y, y], y) == 2)