# Path: dsenser/nnbase.py
# MAX = 1e10
# INF = float('inf')
# UNK_PROB = lambda: np.random.binomial(1, 0.05)
# DIG_RE = re.compile(r"^[\d.]*\d[\d.]*$")
# def _norm_vec(a_x):
# def _norm_word(a_word):
#     def __init__(self, a_w2v=False, a_lstsq=False, a_max_iters=MAX_ITERS):
#     def train(self, a_train_data, a_dev_data=None, a_n_y=-1,
#               a_i=-1, a_train_out=None, a_dev_out=None):
#     def _init_nn(self):
#     def predict(self, a_rel, a_data, a_ret, a_i):
#     def _predict(self, a_args, a_ret, a_i):
#     def _free(self):
#     def _cleanup(self, a_vars):
#     def _generate_ts(self, a_data, a_get_w_emb_i, a_get_c_emb_i):
#     def _rel2x(self, a_rel, a_parses, a_get_w_emb_i, a_get_c_emb_i):
#     def _arg2emb_idx(self, a_parses, a_rel, a_arg, a_get_emb_i):
#     def _init_w_emb(self):
#     def _init_w2v_emb(self):
#     def _init_w2emb(self):
#     def _get_train_w_emb_i(self, a_word):
#     def _get_test_w_emb_i(self, a_word):
#     def _get_train_w2v_emb_i(self, a_word):
#     def _get_test_w2v_emb_i(self, a_word):
#     def _get_test_w2v_lstsq_emb_i(self, a_word):
#     def _init_conn_emb(self):
#     def get_train_c_emb_i(self, a_conn):
#     def get_test_c_emb_i(self, a_conn):
#     def _init_dropout(self, a_input):
#     def _init_funcs(self, a_grads=None):
#     def _init_wemb_funcs(self):
#     def _reset_funcs(self):
#     def _compute_w_stat(self, a_parses):
# class NNBaseSenser(BaseSenser):
# 
# Path: dsenser/constants.py
# CONNECTIVE = "Connective"
# 
# WORDS = "words"
# 
# SENTENCES = "sentences"
# 
# RAW_TEXT = "RawText"


from dsenser.nnbase import _norm_vec, _norm_word, NNBaseSenser, DFLT_VDIM
from dsenser.constants import CONNECTIVE, WORDS, SENTENCES, RAW_TEXT
from pytest import fixture
from mock import patch, MagicMock, Mock
from unittest import TestCase
import dsenser
import dsenser.nnbase
import numpy as np
import pytest
import theano
#!/usr/bin/env python
# -*- coding: utf-8; mode: python; -*-

##################################################################
# Imports
from __future__ import absolute_import




##################################################################
# Constants
DOC_ID = "wsj_2200"
PARSE1 = {DOC_ID: {
    SENTENCES: [{WORDS: []}, {WORDS: []},

# Path: dsenser/nnbase.py
# MAX = 1e10
# INF = float('inf')
# UNK_PROB = lambda: np.random.binomial(1, 0.05)
# DIG_RE = re.compile(r"^[\d.]*\d[\d.]*$")
# def _norm_vec(a_x):
# def _norm_word(a_word):
#     def __init__(self, a_w2v=False, a_lstsq=False, a_max_iters=MAX_ITERS):
#     def train(self, a_train_data, a_dev_data=None, a_n_y=-1,
#               a_i=-1, a_train_out=None, a_dev_out=None):
#     def _init_nn(self):
#     def predict(self, a_rel, a_data, a_ret, a_i):
#     def _predict(self, a_args, a_ret, a_i):
#     def _free(self):
#     def _cleanup(self, a_vars):
#     def _generate_ts(self, a_data, a_get_w_emb_i, a_get_c_emb_i):
#     def _rel2x(self, a_rel, a_parses, a_get_w_emb_i, a_get_c_emb_i):
#     def _arg2emb_idx(self, a_parses, a_rel, a_arg, a_get_emb_i):
#     def _init_w_emb(self):
#     def _init_w2v_emb(self):
#     def _init_w2emb(self):
#     def _get_train_w_emb_i(self, a_word):
#     def _get_test_w_emb_i(self, a_word):
#     def _get_train_w2v_emb_i(self, a_word):
#     def _get_test_w2v_emb_i(self, a_word):
#     def _get_test_w2v_lstsq_emb_i(self, a_word):
#     def _init_conn_emb(self):
#     def get_train_c_emb_i(self, a_conn):
#     def get_test_c_emb_i(self, a_conn):
#     def _init_dropout(self, a_input):
#     def _init_funcs(self, a_grads=None):
#     def _init_wemb_funcs(self):
#     def _reset_funcs(self):
#     def _compute_w_stat(self, a_parses):
# class NNBaseSenser(BaseSenser):
# 
# Path: dsenser/constants.py
# CONNECTIVE = "Connective"
# 
# WORDS = "words"
# 
# SENTENCES = "sentences"
# 
# RAW_TEXT = "RawText"


# Path: tests/test_nnbase.py
from dsenser.nnbase import _norm_vec, _norm_word, NNBaseSenser, DFLT_VDIM
from dsenser.constants import CONNECTIVE, WORDS, SENTENCES, RAW_TEXT
from pytest import fixture
from mock import patch, MagicMock, Mock
from unittest import TestCase
import dsenser
import dsenser.nnbase
import numpy as np
import pytest
import theano

#!/usr/bin/env python
# -*- coding: utf-8; mode: python; -*-

##################################################################
# Imports
from __future__ import absolute_import




##################################################################
# Constants
DOC_ID = "wsj_2200"
PARSE1 = {DOC_ID: {
    SENTENCES: [{WORDS: []}, {WORDS: []},

                {WORDS: [["The", {"PartOfSpeech": "DT"}],