# Path: dlm/eval.py
# class Evaluator():
# 	def __init__(self, dataset, classifier):
# 	def classification_error(self):
# 	def mean_neg_log_likelihood(self):
# 	def mean_unnormalized_neg_log_likelihood(self):
# 	def perplexity(self):
# 	def unnormalized_perplexity(self):
# 	def get_sequence_log_prob(self, index):
# 	def get_unnormalized_sequence_log_prob(self, index):
# 	def get_ngram_log_prob(self, x, y):
# 	def get_denominator(self):
# 	def get_class(self, index, restricted_ids = []):
# 
# Path: dlm/algorithms/lr_tuner.py
# class LRTuner:
# 	def __init__(self, low, high, inc):
# 		self.low = low
# 		self.high = high
# 		self.inc = inc
# 		self.prev_ppl = numpy.inf
# 	
# 	def adapt_lr(self, curr_ppl, curr_lr):
# 		if curr_ppl >= self.prev_ppl:
# 			lr = max(curr_lr / 2, self.low)
# 		else:
# 			lr = min(curr_lr + self.inc, self.high)
# 		self.prev_ppl = curr_ppl
# 		return lr


import theano
import theano.tensor as T
import dlm.utils as U
import dlm.io.logging as L
import time
import numpy as np
import sys
import time
from dlm import eval
from dlm.algorithms.lr_tuner import LRTuner
		from dlm.algorithms.sgd import SGD as Trainer
from __future__ import division


def train(classifier, criterion, args, trainset, devset, testset=None):
	if args.algorithm == "sgd":
	else:
		L.error("Invalid training algorithm: " + args.algorithm)

	# Get number of minibatches from the training file
	num_train_batches = trainset.get_num_batches()
	
	# Initialize the trainer object
	trainer = Trainer(classifier, criterion, args.learning_rate, trainset, clip_threshold=args.clip_threshold)

	# Initialize the Learning Rate tuner, which adjusts learning rate based on the development/validation file

# Path: dlm/eval.py
# class Evaluator():
# 	def __init__(self, dataset, classifier):
# 	def classification_error(self):
# 	def mean_neg_log_likelihood(self):
# 	def mean_unnormalized_neg_log_likelihood(self):
# 	def perplexity(self):
# 	def unnormalized_perplexity(self):
# 	def get_sequence_log_prob(self, index):
# 	def get_unnormalized_sequence_log_prob(self, index):
# 	def get_ngram_log_prob(self, x, y):
# 	def get_denominator(self):
# 	def get_class(self, index, restricted_ids = []):
# 
# Path: dlm/algorithms/lr_tuner.py
# class LRTuner:
# 	def __init__(self, low, high, inc):
# 		self.low = low
# 		self.high = high
# 		self.inc = inc
# 		self.prev_ppl = numpy.inf
# 	
# 	def adapt_lr(self, curr_ppl, curr_lr):
# 		if curr_ppl >= self.prev_ppl:
# 			lr = max(curr_lr / 2, self.low)
# 		else:
# 			lr = min(curr_lr + self.inc, self.high)
# 		self.prev_ppl = curr_ppl
# 		return lr


# Path: dlm/trainer.py
import theano
import theano.tensor as T
import dlm.utils as U
import dlm.io.logging as L
import time
import numpy as np
import sys
import time
from dlm import eval
from dlm.algorithms.lr_tuner import LRTuner
		from dlm.algorithms.sgd import SGD as Trainer

from __future__ import division


def train(classifier, criterion, args, trainset, devset, testset=None):
	if args.algorithm == "sgd":
	else:
		L.error("Invalid training algorithm: " + args.algorithm)

	# Get number of minibatches from the training file
	num_train_batches = trainset.get_num_batches()
	
	# Initialize the trainer object
	trainer = Trainer(classifier, criterion, args.learning_rate, trainset, clip_threshold=args.clip_threshold)

	# Initialize the Learning Rate tuner, which adjusts learning rate based on the development/validation file

	lr_tuner = LRTuner(low=0.01*args.learning_rate, high=10*args.learning_rate, inc=0.01*args.learning_rate)