SYMBOL INDEX (584 symbols across 34 files)

FILE: emolga/basic/activations.py
  function softmax (line 4) | def softmax(x):
  function vector_softmax (line 8) | def vector_softmax(x):
  function time_distributed_softmax (line 12) | def time_distributed_softmax(x):
  function softplus (line 18) | def softplus(x):
  function relu (line 22) | def relu(x):
  function tanh (line 26) | def tanh(x):
  function sigmoid (line 30) | def sigmoid(x):
  function hard_sigmoid (line 34) | def hard_sigmoid(x):
  function linear (line 38) | def linear(x):
  function maxout2 (line 45) | def maxout2(x):
  function get (line 68) | def get(identifier):

FILE: emolga/basic/initializations.py
  function get_fans (line 8) | def get_fans(shape):
  function uniform (line 16) | def uniform(shape, scale=0.1):
  function normal (line 20) | def normal(shape, scale=0.05):
  function lecun_uniform (line 24) | def lecun_uniform(shape):
  function glorot_normal (line 33) | def glorot_normal(shape):
  function glorot_uniform (line 41) | def glorot_uniform(shape):
  function he_normal (line 47) | def he_normal(shape):
  function he_uniform (line 55) | def he_uniform(shape):
  function orthogonal (line 61) | def orthogonal(shape, scale=1.1):
  function identity (line 73) | def identity(shape, scale=1):
  function zero (line 80) | def zero(shape):
  function one (line 84) | def one(shape):
  function get (line 88) | def get(identifier):

FILE: emolga/basic/objectives.py
  function mean_squared_error (line 13) | def mean_squared_error(y_true, y_pred):
  function mean_absolute_error (line 17) | def mean_absolute_error(y_true, y_pred):
  function mean_absolute_percentage_error (line 21) | def mean_absolute_percentage_error(y_true, y_pred):
  function mean_squared_logarithmic_error (line 25) | def mean_squared_logarithmic_error(y_true, y_pred):
  function squared_hinge (line 29) | def squared_hinge(y_true, y_pred):
  function hinge (line 33) | def hinge(y_true, y_pred):
  function categorical_crossentropy (line 37) | def categorical_crossentropy(y_true, y_pred):
  function binary_crossentropy (line 47) | def binary_crossentropy(y_true, y_pred):
  function poisson_loss (line 53) | def poisson_loss(y_true, y_pred):
  function gaussian_kl_divergence (line 59) | def gaussian_kl_divergence(mean, ln_var):
  function get (line 97) | def get(identifier):

FILE: emolga/basic/optimizers.py
  function clip_norm (line 17) | def clip_norm(g, c, n):
  function kl_divergence (line 23) | def kl_divergence(p, p_hat):
  class Optimizer (line 27) | class Optimizer(object):
    method __init__ (line 28) | def __init__(self, **kwargs):
    method add (line 33) | def add(self, v):
    method get_state (line 36) | def get_state(self):
    method set_state (line 39) | def set_state(self, value_list):
    method get_updates (line 44) | def get_updates(self, params, loss):
    method get_gradients (line 47) | def get_gradients(self, loss, params):
    method get_config (line 66) | def get_config(self):
  class SGD (line 70) | class SGD(Optimizer):
    method __init__ (line 72) | def __init__(self, lr=0.05, momentum=0.9, decay=0.01, nesterov=True, *...
    method get_updates (line 79) | def get_updates(self, params, loss):
    method get_config (line 97) | def get_config(self):
  class RMSprop (line 105) | class RMSprop(Optimizer):
    method __init__ (line 106) | def __init__(self, lr=0.001, rho=0.9, epsilon=1e-6, *args, **kwargs):
    method get_updates (line 113) | def get_updates(self, params, loss):
    method get_config (line 126) | def get_config(self):
  class Adagrad (line 133) | class Adagrad(Optimizer):
    method __init__ (line 134) | def __init__(self, lr=0.01, epsilon=1e-6, *args, **kwargs):
    method get_updates (line 139) | def get_updates(self, params, constraints, loss):
    method get_config (line 151) | def get_config(self):
  class Adadelta (line 157) | class Adadelta(Optimizer):
    method __init__ (line 161) | def __init__(self, lr=0.1, rho=0.95, epsilon=1e-6, *args, **kwargs):
    method get_updates (line 167) | def get_updates(self, params, loss):
    method get_config (line 190) | def get_config(self):
  class Adam (line 197) | class Adam(Optimizer):  # new Adam is designed for our purpose.
    method __init__ (line 204) | def __init__(self, lr=1e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-8, sa...
    method add_noise (line 230) | def add_noise(self, param):
    method add_forget (line 235) | def add_forget(self, param):
    method get_updates (line 240) | def get_updates(self, params, loss):
    method get_config (line 282) | def get_config(self):
  function get (line 304) | def get(identifier, kwargs=None):

FILE: emolga/dataset/build_dataset.py
  function serialize_to_file_json (line 17) | def serialize_to_file_json(obj, path, protocol=pickle.HIGHEST_PROTOCOL):
  function serialize_to_file_hdf5 (line 22) | def serialize_to_file_hdf5(obj, path, protocol=pickle.HIGHEST_PROTOCOL):
  function serialize_to_file (line 27) | def serialize_to_file(obj, path, protocol=pickle.HIGHEST_PROTOCOL):
  function show_txt (line 34) | def show_txt(array, path):
  function divide_dataset (line 42) | def divide_dataset(dataset, test_size, max_size):
  function deserialize_from_file_json (line 52) | def deserialize_from_file_json(path):
  function deserialize_from_file_hdf5 (line 58) | def deserialize_from_file_hdf5(path):
  function deserialize_from_file (line 64) | def deserialize_from_file(path):
  function build_fuel (line 71) | def build_fuel(data):
  function obtain_stream (line 79) | def obtain_stream(dataset, batch_size, size=1):
  function build_ptb (line 94) | def build_ptb():
  function filter_unk (line 123) | def filter_unk(X, min_freq=5):
  function build_msr (line 149) | def build_msr():

FILE: emolga/layers/attention.py
  class Attention (line 11) | class Attention(Layer):
    method __init__ (line 12) | def __init__(self, target_dim, source_dim, hidden_dim,
    method __call__ (line 42) | def __call__(self, X, S,
  class CosineAttention (line 82) | class CosineAttention(Layer):
    method __init__ (line 83) | def __init__(self, target_dim, source_dim,
    method __call__ (line 117) | def __call__(self, X, S, Smask=None, return_log=False):

FILE: emolga/layers/core.py
  class Layer (line 8) | class Layer(object):
    method __init__ (line 9) | def __init__(self):
    method init_updates (line 15) | def init_updates(self):
    method _monitoring (line 18) | def _monitoring(self):
    method __call__ (line 26) | def __call__(self, X, *args, **kwargs):
    method _add (line 29) | def _add(self, layer):
    method supports_masked_input (line 34) | def supports_masked_input(self):
    method get_output_mask (line 40) | def get_output_mask(self, train=None):
    method set_weights (line 56) | def set_weights(self, weights):
    method get_weights (line 62) | def get_weights(self):
    method get_params (line 68) | def get_params(self):
    method set_name (line 71) | def set_name(self, name):
  class MaskedLayer (line 80) | class MaskedLayer(Layer):
    method supports_masked_input (line 85) | def supports_masked_input(self):
  class Identity (line 89) | class Identity(Layer):
    method __init__ (line 90) | def __init__(self, name='Identity'):
    method __call__ (line 95) | def __call__(self, X):
  class Dense (line 99) | class Dense(Layer):
    method __init__ (line 100) | def __init__(self, input_dim, output_dim, init='glorot_uniform', activ...
    method set_name (line 126) | def set_name(self, name):
    method __call__ (line 130) | def __call__(self, X):
    method reverse (line 135) | def reverse(self, Y):
  class Dense2 (line 142) | class Dense2(Layer):
    method __init__ (line 143) | def __init__(self, input_dim1, input_dim2, output_dim, init='glorot_un...
    method set_name (line 168) | def set_name(self, name):
    method __call__ (line 173) | def __call__(self, X1, X2):
  class Constant (line 178) | class Constant(Layer):
    method __init__ (line 179) | def __init__(self, input_dim, output_dim, init=None, activation='tanh'...
    method set_name (line 195) | def set_name(self, name):
    method __call__ (line 198) | def __call__(self, X=None):
  class MemoryLinear (line 206) | class MemoryLinear(Layer):
    method __init__ (line 207) | def __init__(self, input_dim, input_wdth, init='glorot_uniform',
    method __call__ (line 226) | def __call__(self, X=None):
  class Dropout (line 233) | class Dropout(MaskedLayer):
    method __init__ (line 237) | def __init__(self, rng=None, p=1., name=None):
    method __call__ (line 242) | def __call__(self, X, train=True):
  class Activation (line 252) | class Activation(MaskedLayer):
    method __init__ (line 256) | def __init__(self, activation):
    method __call__ (line 260) | def __call__(self, X):

FILE: emolga/layers/embeddings.py
  class Embedding (line 8) | class Embedding(Layer):
    method __init__ (line 17) | def __init__(self, input_dim, output_dim, init='uniform', name=None):
    method get_output_mask (line 31) | def get_output_mask(self, X):
    method __call__ (line 40) | def __call__(self, X, mask_zero=False, context=None):
  class Zero (line 85) | class Zero(Layer):
    method __call__ (line 86) | def __call__(self, X):
  class Bias (line 91) | class Bias(Layer):
    method __call__ (line 92) | def __call__(self, X):

FILE: emolga/layers/gridlstm.py
  class Grid (line 13) | class Grid(Recurrent):
    method __init__ (line 41) | def __init__(self,
    method build (line 86) | def build(self):
    method lstm_ (line 140) | def lstm_(self, k, H, m, x, identity=False):
    method grid_ (line 195) | def grid_(self,
  class SequentialGridLSTM (line 243) | class SequentialGridLSTM(Grid):
    method __init__ (line 253) | def __init__(self,
    method _step (line 362) | def _step(self, *args):
    method __call__ (line 413) | def __call__(self, X, init_H=None, init_M=None,
  class PyramidGridLSTM2D (line 476) | class PyramidGridLSTM2D(Grid):
    method __init__ (line 480) | def __init__(self,
    method _step (line 565) | def _step(self, *args):
    method __call__ (line 618) | def __call__(self, X, init_x=None, init_y=None,
  class PyramidLSTM (line 669) | class PyramidLSTM(Layer):
    method __init__ (line 673) | def __init__(self,
    method _step (line 768) | def _step(self, *args):
    method __call__ (line 826) | def __call__(self, X, init_x=None, init_y=None,

FILE: emolga/layers/ntm_minibatch.py
  class Reader (line 17) | class Reader(Layer):
    method __init__ (line 22) | def __init__(self, input_dim, memory_width, shift_width, shift_conv,
    method __call__ (line 67) | def __call__(self, X, w_temp, m_temp):
  class Writer (line 93) | class Writer(Reader):
    method __init__ (line 98) | def __init__(self, input_dim, memory_width, shift_width, shift_conv,
    method get_fixer (line 119) | def get_fixer(self, X):
  class Controller (line 125) | class Controller(Recurrent):
    method __init__ (line 134) | def __init__(self,
    method _controller (line 216) | def _controller(self, input_t, read_t, controller_tm1=None):
    method _read (line 231) | def _read(w_read, memory):
    method _write (line 239) | def _write(w_write, memory, erase, add):
    method _step (line 252) | def _step(self, input_t, mask_t,
    method __call__ (line 302) | def __call__(self, X, mask=None, M=None, init_ww=None,
  class AttentionReader (line 369) | class AttentionReader(Layer):
    method __init__ (line 374) | def __init__(self, input_dim, memory_width, shift_width, shift_conv,
    method __call__ (line 424) | def __call__(self, X, w_temp, m_temp):
  class AttentionWriter (line 454) | class AttentionWriter(AttentionReader):
    method __init__ (line 459) | def __init__(self, input_dim, memory_width, shift_width, shift_conv,
    method get_fixer (line 480) | def get_fixer(self, X):
  class BernoulliController (line 487) | class BernoulliController(Recurrent):
    method __init__ (line 496) | def __init__(self,
    method _controller (line 579) | def _controller(self, input_t, read_t, controller_tm1=None):
    method _read (line 594) | def _read(w_read, memory):
    method _write (line 602) | def _write(w_write, memory, erase, add):
    method _step (line 618) | def _step(self, input_t, mask_t,
    method __call__ (line 668) | def __call__(self, X, mask=None, M=None, init_ww=None,

FILE: emolga/layers/recurrent.py
  class Recurrent (line 6) | class Recurrent(MaskedLayer):
    method get_padded_shuffled_mask (line 12) | def get_padded_shuffled_mask(mask, pad=0):
  class GRU (line 37) | class GRU(Recurrent):
    method __init__ (line 71) | def __init__(self,
    method _step (line 132) | def _step(self,
    method _step_gate (line 165) | def _step_gate(self,
    method __call__ (line 185) | def __call__(self, X, mask=None, C=None, init_h=None,
  class JZS3 (line 278) | class JZS3(Recurrent):
    method __init__ (line 298) | def __init__(self,
    method _step (line 359) | def _step(self,
    method __call__ (line 370) | def __call__(self, X, mask=None, C=None, init_h=None, return_sequence=...
  class LSTM (line 425) | class LSTM(Recurrent):
    method __init__ (line 426) | def __init__(self,
    method _step (line 500) | def _step(self,
    method input_embed (line 520) | def input_embed(self, X, C=None):
    method __call__ (line 539) | def __call__(self, X, mask=None, C=None, init_h=None, init_c=None, ret...

FILE: emolga/models/core.py
  class Model (line 18) | class Model(object):
    method __init__ (line 19) | def __init__(self):
    method _add (line 25) | def _add(self, layer):
    method _monitoring (line 30) | def _monitoring(self):
    method compile_monitoring (line 38) | def compile_monitoring(self, inputs, updates=None):
    method set_weights (line 50) | def set_weights(self, weights):
    method get_weights (line 62) | def get_weights(self):
    method set_name (line 73) | def set_name(self, name):
    method save (line 81) | def save(self, filename):
    method load (line 96) | def load(self, filename):
    method save_weight_json (line 105) | def save_weight_json(self, filename):
    method load_weight_json (line 117) | def load_weight_json(self, filename):

FILE: emolga/models/covc_encdec.py
  class Encoder (line 24) | class Encoder(Model):
    method __init__ (line 30) | def __init__(self,
    method build_encoder (line 104) | def build_encoder(self, source, context=None, return_embed=False,
    method compile_encoder (line 214) | def compile_encoder(self, with_context=False, return_embed=False, retu...
  class Decoder (line 259) | class Decoder(Model):
    method __init__ (line 269) | def __init__(self,
    method _grab_prob (line 435) | def _grab_prob(probs, X, block_unk=False):
    method prepare_xy (line 448) | def prepare_xy(self, target):
    method build_decoder (line 463) | def build_decoder(self, target, context=None,
    method _step_sample (line 544) | def _step_sample(self, prev_word, prev_stat, context):
    method build_sampler (line 598) | def build_sampler(self):
    method build_stochastic_sampler (line 640) | def build_stochastic_sampler(self):
    method get_sample (line 651) | def get_sample(self, context, k=1, maxlen=30, stochastic=True, argmax=...
  class DecoderAtt (line 762) | class DecoderAtt(Decoder):
    method __init__ (line 767) | def __init__(self,
    method prepare_xy (line 827) | def prepare_xy(self, target, cc_matrix):
    method build_decoder (line 875) | def build_decoder(self,
    method _step_sample (line 1051) | def _step_sample(self,
    method build_sampler (line 1155) | def build_sampler(self):
    method get_sample (line 1209) | def get_sample(self,
  class FnnDecoder (line 1491) | class FnnDecoder(Model):
    method __init__ (line 1492) | def __init__(self, config, rng, prefix='fnndec'):
    method _grab_prob (line 1520) | def _grab_prob(probs, X):
    method build_decoder (line 1530) | def build_decoder(self, target, context):
    method build_sampler (line 1538) | def build_sampler(self):
    method get_sample (line 1545) | def get_sample(self, context, argmax=True):
  class RNNLM (line 1557) | class RNNLM(Model):
    method __init__ (line 1562) | def __init__(self,
    method build_ (line 1573) | def build_(self):
    method compile_ (line 1590) | def compile_(self, mode='train', contrastive=False):
    method compile_train (line 1616) | def compile_train(self):
    method compile_train_CE (line 1659) | def compile_train_CE(self):
    method compile_sample (line 1662) | def compile_sample(self):
    method compile_inference (line 1668) | def compile_inference(self):
    method default_context (line 1671) | def default_context(self):
    method generate_ (line 1679) | def generate_(self, context=None, max_len=None, mode='display'):
  class AutoEncoder (line 1707) | class AutoEncoder(RNNLM):
    method __init__ (line 1712) | def __init__(self,
    method build_ (line 1723) | def build_(self):
    method compile_train (line 1769) | def compile_train(self, mode='train'):
  class NRM (line 1813) | class NRM(Model):
    method __init__ (line 1818) | def __init__(self,
    method build_ (line 1835) | def build_(self, lr=None, iterations=None):
    method compile_ (line 1863) | def compile_(self, mode='all', contrastive=False):
    method compile_train (line 1886) | def compile_train(self):
    method compile_sample (line 1950) | def compile_sample(self):
    method compile_inference (line 1959) | def compile_inference(self):
    method generate_ (line 1962) | def generate_(self, inputs, mode='display', return_attend=False, retur...
    method generate_multiple (line 2006) | def generate_multiple(self, inputs, mode='display', return_attend=Fals...
    method evaluate_ (line 2060) | def evaluate_(self, inputs, outputs, idx2word, inputs_unk=None, encode...
    method evaluate_multiple (line 2089) | def evaluate_multiple(self, inputs, outputs,
    method analyse_ (line 2377) | def analyse_(self, inputs, outputs, idx2word, inputs_unk=None, return_...
    method analyse_cover (line 2434) | def analyse_cover(self, inputs, outputs, idx2word, inputs_unk=None, re...

FILE: emolga/models/encdec.py
  class Encoder (line 177) | class Encoder(Model):
    method __init__ (line 183) | def __init__(self,
    method build_encoder (line 257) | def build_encoder(self, source, context=None, return_embed=False, retu...
    method compile_encoder (line 319) | def compile_encoder(self, with_context=False, return_embed=False, retu...
  class Decoder (line 337) | class Decoder(Model):
    method __init__ (line 347) | def __init__(self,
    method _grab_prob (line 505) | def _grab_prob(probs, X):
    method prepare_xy (line 530) | def prepare_xy(self, target):
    method build_decoder (line 545) | def build_decoder(self, target, context=None,
    method _step_sample (line 620) | def _step_sample(self, prev_word, prev_stat, context):
    method build_sampler (line 678) | def build_sampler(self):
    method build_stochastic_sampler (line 721) | def build_stochastic_sampler(self):
    method get_sample (line 732) | def get_sample(self, context, k=1, maxlen=30, stochastic=True, argmax=...
  class DecoderAtt (line 843) | class DecoderAtt(Decoder):
    method __init__ (line 848) | def __init__(self,
    method prepare_xy (line 889) | def prepare_xy(self, target, context=None):
    method build_decoder (line 924) | def build_decoder(self,
    method build_representer (line 1035) | def build_representer(self,
    method _step_sample (line 1152) | def _step_sample(self, prev_word, prev_stat, context, c_mask):
    method build_sampler (line 1217) | def build_sampler(self):
    method get_sample (line 1256) | def get_sample(self, encoding, c_mask, inputs,
  class FnnDecoder (line 1448) | class FnnDecoder(Model):
    method __init__ (line 1449) | def __init__(self, config, rng, prefix='fnndec'):
    method _grab_prob (line 1477) | def _grab_prob(probs, X):
    method build_decoder (line 1487) | def build_decoder(self, target, context):
    method build_sampler (line 1495) | def build_sampler(self):
    method get_sample (line 1502) | def get_sample(self, context, argmax=True):
  class RNNLM (line 1514) | class RNNLM(Model):
    method __init__ (line 1519) | def __init__(self,
    method build_ (line 1530) | def build_(self):
    method compile_ (line 1547) | def compile_(self, mode='train', contrastive=False):
    method compile_train (line 1573) | def compile_train(self):
    method compile_train_CE (line 1617) | def compile_train_CE(self):
    method compile_sample (line 1620) | def compile_sample(self):
    method compile_inference (line 1626) | def compile_inference(self):
    method default_context (line 1629) | def default_context(self):
    method generate_ (line 1637) | def generate_(self, context=None, max_len=None, mode='display'):
  class AutoEncoder (line 1665) | class AutoEncoder(RNNLM):
    method __init__ (line 1670) | def __init__(self,
    method build_ (line 1681) | def build_(self):
    method compile_train (line 1727) | def compile_train(self, mode='train'):
  class NRM (line 1770) | class NRM(Model):
    method __init__ (line 1775) | def __init__(self,
    method build_ (line 1792) | def build_(self):
    method compile_ (line 1815) | def compile_(self, mode='all', contrastive=False):
    method compile_train (line 1838) | def compile_train(self):
    method compile_sample (line 1902) | def compile_sample(self):
    method compile_inference (line 1911) | def compile_inference(self):
    method generate_ (line 1914) | def generate_(self, inputs, mode='display', return_all=False):
    method generate_multiple (line 1951) | def generate_multiple(self, inputs, mode='display', return_all=True, a...
    method evaluate_ (line 2030) | def evaluate_(self, inputs, outputs, idx2word, inputs_unk=None):
    method evaluate_multiple (line 2057) | def evaluate_multiple(self, inputs, outputs,
    method analyse_ (line 2281) | def analyse_(self, inputs, outputs, idx2word):
    method analyse_cover (line 2298) | def analyse_cover(self, inputs, outputs, idx2word):

FILE: emolga/models/ntm_encdec.py
  class RecurrentBase (line 20) | class RecurrentBase(Model):
    method __init__ (line 24) | def __init__(self, config, model='RNN', prefix='enc', use_contxt=True,...
    method get_context (line 105) | def get_context(self, context):
    method loop (line 127) | def loop(self, X, X_mask, info=None, return_sequence=False, return_ful...
    method step (line 135) | def step(self, X, prev_info):
    method build_ (line 153) | def build_(self):
    method get_init (line 209) | def get_init(self, context):
    method get_next_state (line 228) | def get_next_state(self, prev_X, prev_info):
  class Encoder (line 255) | class Encoder(Model):
    method __init__ (line 261) | def __init__(self,
    method build_encoder (line 306) | def build_encoder(self, source, context=None):
  class Decoder (line 347) | class Decoder(Model):
    method __init__ (line 357) | def __init__(self,
    method _grab_prob (line 462) | def _grab_prob(probs, X):
    method prepare_xy (line 475) | def prepare_xy(self, target):
    method build_decoder (line 490) | def build_decoder(self, target, context=None, return_count=False):
    method _step_embed (line 527) | def _step_embed(self, prev_word):
    method _step_sample (line 540) | def _step_sample(self, X, next_stat, context):
    method build_sampler (line 563) | def build_sampler(self):
    method get_sample (line 595) | def get_sample(self, context, k=1, maxlen=30, stochastic=True, argmax=...
  class RNNLM (line 717) | class RNNLM(Model):
    method __init__ (line 722) | def __init__(self,
    method build_ (line 733) | def build_(self):
    method compile_ (line 749) | def compile_(self, mode='train', contrastive=False):
    method compile_train (line 775) | def compile_train(self):
    method compile_train_CE (line 815) | def compile_train_CE(self):
    method compile_sample (line 818) | def compile_sample(self):
    method compile_inference (line 823) | def compile_inference(self):
    method default_context (line 826) | def default_context(self):
    method generate_ (line 834) | def generate_(self, context=None, mode='display', max_len=None):
  class Helmholtz (line 862) | class Helmholtz(RNNLM):
    method __init__ (line 871) | def __init__(self,
    method build_ (line 882) | def build_(self):
    method compile_train (line 951) | def compile_train(self):
    method compile_sample (line 1053) | def compile_sample(self):
    method compile_inference (line 1077) | def compile_inference(self):
    method default_context (line 1100) | def default_context(self):
  class BinaryHelmholtz (line 1105) | class BinaryHelmholtz(RNNLM):
    method __init__ (line 1114) | def __init__(self,
    method build_ (line 1125) | def build_(self):
    method compile_train (line 1175) | def compile_train(self):
    method compile_sample (line 1277) | def compile_sample(self):
    method compile_inference (line 1300) | def compile_inference(self):
    method default_context (line 1323) | def default_context(self):
  class AutoEncoder (line 1328) | class AutoEncoder(RNNLM):
    method __init__ (line 1334) | def __init__(self,
    method build_ (line 1345) | def build_(self):
    method compile_train (line 1368) | def compile_train(self, mode='train'):
    method compile_sample (line 1404) | def compile_sample(self):

FILE: emolga/models/pointers.py
  class PtrDecoder (line 19) | class PtrDecoder(Model):
    method __init__ (line 23) | def __init__(self,
    method grab_prob (line 64) | def grab_prob(probs, X):
    method grab_source (line 75) | def grab_source(source, target):
    method build_decoder (line 91) | def build_decoder(self,
    method _step_sample (line 139) | def _step_sample(self, prev_idx, prev_stat,
    method build_sampler (line 156) | def build_sampler(self):
    method get_sample (line 199) | def get_sample(self, context, inputs, source, smask,
  class PointerDecoder (line 316) | class PointerDecoder(Model):
    method __init__ (line 321) | def __init__(self,
    method grab_prob (line 370) | def grab_prob(probs, X):
    method grab_source (line 381) | def grab_source(source, target):
    method build_decoder (line 397) | def build_decoder(self,
    method _step_sample (line 473) | def _step_sample(self,
    method build_sampler (line 495) | def build_sampler(self):
    method get_sample (line 540) | def get_sample(self, context, inputs, source, smask,
  class MemNet (line 616) | class MemNet(Model):
    method __init__ (line 621) | def __init__(self,
    method __call__ (line 653) | def __call__(self, key, memory=None, mem_mask=None, out_memory=None):
  class PtrNet (line 675) | class PtrNet(Model):
    method __init__ (line 679) | def __init__(self, config, n_rng, rng,
    method build_ (line 689) | def build_(self, encoder=None):
    method build_train (line 743) | def build_train(self, memory=None, out_memory=None, compile_train=Fals...
    method build_sampler (line 881) | def build_sampler(self, memory=None, out_mem=None):
    method build_predict_sampler (line 925) | def build_predict_sampler(self):
    method generate_ (line 973) | def generate_(self, inputs, context, source, smask):

FILE: emolga/models/variational.py
  class VAE (line 19) | class VAE(RNNLM):
    method __init__ (line 33) | def __init__(self,
    method _add_tag (line 45) | def _add_tag(self, layer, tag):
    method build_ (line 52) | def build_(self):
    method compile_train (line 115) | def compile_train(self):
    method compile_sample (line 176) | def compile_sample(self):
    method compile_inference (line 192) | def compile_inference(self):
    method default_context (line 208) | def default_context(self):
  class Helmholtz (line 212) | class Helmholtz(VAE):
    method __init__ (line 220) | def __init__(self,
    method build_ (line 235) | def build_(self):
    method dynamic (line 301) | def dynamic(self):
    method compile_ (line 330) | def compile_(self, mode='train', contrastive=False):
    method compile_train (line 356) | def compile_train(self):
    method build_dynamics (line 477) | def build_dynamics(self, states, action, Y):
    method compile_sample (line 487) | def compile_sample(self):
    method compile_inference (line 523) | def compile_inference(self):
    method evaluate_ (line 539) | def evaluate_(self, inputs):
    method compile_train_CE (line 564) | def compile_train_CE(self):
  class HarX (line 695) | class HarX(Helmholtz):
    method __init__ (line 705) | def __init__(self,
    method build_ (line 720) | def build_(self):
    method compile_ (line 798) | def compile_(self, mode='train', contrastive=False):
    method compile_train (line 824) | def compile_train(self):
    method generate_ (line 1015) | def generate_(self, context=None, max_len=None, mode='display'):
  class THarX (line 1023) | class THarX(Helmholtz):
    method __init__ (line 1033) | def __init__(self,
    method build_ (line 1048) | def build_(self):
    method compile_ (line 1126) | def compile_(self, mode='train', contrastive=False):
    method compile_train (line 1152) | def compile_train(self):
    method generate_ (line 1350) | def generate_(self, context=None, max_len=None, mode='display'):
  class NVTM (line 1358) | class NVTM(Helmholtz):
    method __init__ (line 1364) | def __init__(self,
    method build_ (line 1376) | def build_(self):
    method compile_ (line 1492) | def compile_(self, mode='train', contrastive=False):
    method compile_train (line 1518) | def compile_train(self):
    method generate_ (line 1701) | def generate_(self, context=None, max_len=None, mode='display'):

FILE: emolga/utils/generic_utils.py
  function get_from_module (line 10) | def get_from_module(identifier, module_params, module_name, instantiate=...
  function make_tuple (line 24) | def make_tuple(*args):
  function printv (line 27) | def printv(v, prefix=''):
  function make_batches (line 49) | def make_batches(size, batch_size):
  function slice_X (line 54) | def slice_X(X, start=None, stop=None):
  class Progbar (line 67) | class Progbar(object):
    method __init__ (line 68) | def __init__(self, target, logger, width=30, verbose=1):
    method update (line 83) | def update(self, current, values=[]):
    method add (line 158) | def add(self, n, values=[]):
    method clear (line 161) | def clear(self):
  function print_sample (line 168) | def print_sample(idx2word, idx):
  function visualize_ (line 178) | def visualize_(subplots, data, w=None, h=None, name=None,
  function vis_Gaussian (line 255) | def vis_Gaussian(subplot, mean, std, name=None, display='off', size=10):

FILE: emolga/utils/io_utils.py
  class HDF5Matrix (line 8) | class HDF5Matrix():
    method __init__ (line 11) | def __init__(self, datapath, dataset, start, end, normalizer=None):
    method __len__ (line 22) | def __len__(self):
    method __getitem__ (line 25) | def __getitem__(self, key):
    method shape (line 52) | def shape(self):
  function save_array (line 56) | def save_array(array, name):
  function load_array (line 65) | def load_array(name):
  function save_config (line 75) | def save_config():
  function load_config (line 79) | def load_config():

FILE: emolga/utils/np_utils.py
  function to_categorical (line 8) | def to_categorical(y, nb_classes=None):
  function normalize (line 21) | def normalize(a, axis=-1, order=2):
  function binary_logloss (line 27) | def binary_logloss(p, y):
  function multiclass_logloss (line 36) | def multiclass_logloss(P, Y):
  function accuracy (line 43) | def accuracy(p, y):
  function probas_to_classes (line 47) | def probas_to_classes(y_pred):
  function categorical_probas_to_classes (line 53) | def categorical_probas_to_classes(p):

FILE: emolga/utils/test_utils.py
  function get_test_data (line 4) | def get_test_data(nb_train=1000, nb_test=500, input_shape=(10,), output_...

FILE: emolga/utils/theano_utils.py
  function floatX (line 10) | def floatX(X):
  function sharedX (line 14) | def sharedX(X, dtype=theano.config.floatX, name=None):
  function shared_zeros (line 18) | def shared_zeros(shape, dtype=theano.config.floatX, name=None):
  function shared_scalar (line 22) | def shared_scalar(val=0., dtype=theano.config.floatX, name=None):
  function shared_ones (line 26) | def shared_ones(shape, dtype=theano.config.floatX, name=None):
  function alloc_zeros_matrix (line 30) | def alloc_zeros_matrix(*dims):
  function alloc_ones_matrix (line 34) | def alloc_ones_matrix(*dims):
  function ndim_tensor (line 38) | def ndim_tensor(ndim):
  function ndim_itensor (line 51) | def ndim_itensor(ndim, name=None):
  function dot (line 62) | def dot(inp, matrix, bias=None):
  function logSumExp (line 87) | def logSumExp(x, axis=None, mask=None, status='theano', c=None, err=1e-7):
  function softmax (line 121) | def softmax(x):
  function masked_softmax (line 125) | def masked_softmax(x, mask, err=1e-7):
  function cosine_sim (line 133) | def cosine_sim(k, M):
  function cosine_sim2d (line 144) | def cosine_sim2d(k, M):
  function dot_2d (line 160) | def dot_2d(k, M, b=None, g=None):
  function shift_convolve (line 182) | def shift_convolve(weight, shift, shift_conv):
  function shift_convolve2d (line 187) | def shift_convolve2d(weight, shift, shift_conv):

FILE: keyphrase/baseline/evaluate.py
  function load_phrase (line 19) | def load_phrase(file_path, tokenize=True):
  function evaluate_ (line 30) | def evaluate_(text_dir, target_dir, prediction_dir, model_name, dataset_...
  function init_logging (line 304) | def init_logging(logfile):
  function evaluate_baselines (line 330) | def evaluate_baselines():
  function significance_test (line 356) | def significance_test():

FILE: keyphrase/baseline/export_dataset.py
  function export_UTD (line 10) | def export_UTD():
  class Document (line 43) | class Document(object):
    method __init__ (line 44) | def __init__(self):
    method __str__ (line 50) | def __str__(self):
  function load_text (line 53) | def load_text(doclist, textdir):
  function load_keyphrase (line 83) | def load_keyphrase(doclist, keyphrasedir):
  function get_doc (line 101) | def get_doc(text_dir, phrase_dir):
  function export_maui (line 114) | def export_maui():
  function export_krapivin_maui (line 169) | def export_krapivin_maui():
  function export_ke20k_testing_maui (line 203) | def export_ke20k_testing_maui():
  function export_ke20k_train_maui (line 219) | def export_ke20k_train_maui():
  function prepare_data_cross_validation (line 245) | def prepare_data_cross_validation(input_dir, output_dir, folds=5):

FILE: keyphrase/config.py
  function setup_keyphrase_stable (line 7) | def setup_keyphrase_stable():
  function setup_keyphrase_train (line 192) | def setup_keyphrase_train():
  function setup_keyphrase_baseline (line 374) | def setup_keyphrase_baseline():

FILE: keyphrase/dataset/dataset_utils.py
  function prepare_text (line 23) | def prepare_text(record, process_type=1):
  function get_tokens (line 44) | def get_tokens(text, process_type=1):
  function process_keyphrase (line 76) | def process_keyphrase(keyword_str):
  function build_data (line 90) | def build_data(data, idx2word, word2idx):
  function load_pairs (line 127) | def load_pairs(records, process_type=1 ,do_filter=False):
  function get_none_phrases (line 179) | def get_none_phrases(source_text, source_postag, max_len):

FILE: keyphrase/dataset/inspec/inspec_export_json.py
  function export_Inspec_tokenized (line 19) | def export_Inspec_tokenized(dir_name, output_name):
  function export_Inspec (line 88) | def export_Inspec(Inspec_input_path, Inspec_output_path):

FILE: keyphrase/dataset/keyphrase_dataset.py
  function get_tokens (line 28) | def get_tokens(text, type=1):
  function load_data (line 62) | def load_data(input_path, tokenize_sentence=True):
  function build_dict (line 126) | def build_dict(wordfreq):
  function build_data (line 148) | def build_data(data, idx2word, word2idx):
  function load_data_and_dict (line 182) | def load_data_and_dict(training_dataset, testing_dataset):
  function export_data_for_maui (line 209) | def export_data_for_maui():

FILE: keyphrase/dataset/keyphrase_test_dataset.py
  class Document (line 30) | class Document(object):
    method __init__ (line 31) | def __init__(self):
    method __str__ (line 37) | def __str__(self):
  class DataLoader (line 41) | class DataLoader(object):
    method __init__ (line 42) | def __init__(self, **kwargs):
    method get_docs (line 48) | def get_docs(self, return_dict=True):
    method __call__ (line 73) | def __call__(self, idx2word, word2idx, type = 1):
    method load_xml (line 106) | def load_xml(self, xmldir):
    method load_text (line 134) | def load_text(self, textdir):
    method load_keyphrase (line 164) | def load_keyphrase(self, keyphrasedir):
    method load_testing_data_postag (line 181) | def load_testing_data_postag(self, word2idx):
    method load_testing_data (line 215) | def load_testing_data(self, word2idx):
  class INSPEC (line 250) | class INSPEC(DataLoader):
    method __init__ (line 251) | def __init__(self, **kwargs):
  class NUS (line 264) | class NUS(DataLoader):
    method __init__ (line 265) | def __init__(self, **kwargs):
    method export (line 275) | def export(self):
    method get_docs (line 312) | def get_docs(self, only_abstract=True, return_dict=True):
  class SemEval (line 397) | class SemEval(DataLoader):
    method __init__ (line 398) | def __init__(self, **kwargs):
  class KRAPIVIN (line 488) | class KRAPIVIN(DataLoader):
    method __init__ (line 489) | def __init__(self, **kwargs):
    method load_text (line 499) | def load_text(self, textdir):
  class KDD (line 528) | class KDD(DataLoader):
    method __init__ (line 529) | def __init__(self, **kwargs):
  class WWW (line 536) | class WWW(DataLoader):
    method __init__ (line 537) | def __init__(self, **kwargs):
  class UMD (line 544) | class UMD(DataLoader):
    method __init__ (line 545) | def __init__(self, **kwargs):
  class DUC (line 552) | class DUC(DataLoader):
    method __init__ (line 553) | def __init__(self, **kwargs):
    method export_text_phrase (line 563) | def export_text_phrase(self):
  class KP20k (line 621) | class KP20k(DataLoader):
    method __init__ (line 622) | def __init__(self, **kwargs):
    method get_docs (line 632) | def get_docs(self, return_dict=True):
  class KP2k_NEW (line 661) | class KP2k_NEW(DataLoader):
    method __init__ (line 665) | def __init__(self, **kwargs):
    method get_docs (line 671) | def get_docs(self, return_dict=True):
  class IRBooks (line 700) | class IRBooks(DataLoader):
    method __init__ (line 701) | def __init__(self, **kwargs):
    method get_docs (line 709) | def get_docs(self, return_dict=True):
  class Quora (line 745) | class Quora(DataLoader):
    method __init__ (line 746) | def __init__(self, **kwargs):
    method get_docs (line 754) | def get_docs(self, return_dict=True):
  function testing_data_loader (line 796) | def testing_data_loader(identifier, kwargs=None):
  function load_additional_testing_data (line 805) | def load_additional_testing_data(testing_names, idx2word, word2idx, conf...
  function check_data (line 848) | def check_data():
  function add_padding (line 941) | def add_padding(data):
  function split_into_multiple_and_padding (line 955) | def split_into_multiple_and_padding(data_s_o, data_t_o):
  function get_postag_with_record (line 967) | def get_postag_with_record(records, pairs):
  function get_postag_with_index (line 995) | def get_postag_with_index(sources, idx2word, word2idx):
  function check_postag (line 1022) | def check_postag(config):

FILE: keyphrase/dataset/keyphrase_train_dataset.py
  function build_dict (line 18) | def build_dict(wordfreq):
  function dump_samples_to_json (line 41) | def dump_samples_to_json(records, file_path):
  function load_data_and_dict (line 53) | def load_data_and_dict(training_dataset):

FILE: keyphrase/dataset/million-paper/preprocess.py
  function load_file (line 17) | def load_file(input_path):

FILE: keyphrase/keyphrase_copynet.py
  class LoggerWriter (line 49) | class LoggerWriter:
    method __init__ (line 50) | def __init__(self, level):
    method write (line 55) | def write(self, message):
    method flush (line 61) | def flush(self):
  function init_logging (line 68) | def init_logging(logfile):
  function output_stream (line 86) | def output_stream(dataset, batch_size, size=1):
  function prepare_batch (line 98) | def prepare_batch(batch, mask, fix_len=None):
  function cc_martix (line 115) | def cc_martix(source, target):
  function unk_filter (line 127) | def unk_filter(data):
  function add_padding (line 144) | def add_padding(data):
  function split_into_multiple_and_padding (line 160) | def split_into_multiple_and_padding(data_s_o, data_t_o):
  function build_data (line 172) | def build_data(data):

FILE: keyphrase/keyphrase_utils.py
  function evaluate_multiple (line 14) | def evaluate_multiple(config, test_set, inputs, outputs,
  function export_keyphrase (line 557) | def export_keyphrase(predictions, text_dir, prediction_dir):

FILE: keyphrase/util/stanford-pos-tagger.py
  class StanfordTagger (line 32) | class StanfordTagger(TaggerI):
    method __init__ (line 46) | def __init__(self, model_filename, path_to_jar=None, encoding='utf8', ...
    method _cmd (line 66) | def _cmd(self):
  function tag (line 70) | def tag(self, tokens):
  function tag_sents (line 75) | def tag_sents(self, sentences):
  function parse_output (line 108) | def parse_output(self, text, sentences=None):
  class StanfordNERTagger (line 119) | class StanfordNERTagger(StanfordTagger):
    method __init__ (line 142) | def __init__(self, *args, **kwargs):