# Path: simpleknn/bigfile.py
# class BigFile:
# 
#     def __init__(self, datadir):
#         self.nr_of_images, self.ndims = map(int, open(os.path.join(datadir,'shape.txt')).readline().split())
#         id_file = os.path.join(datadir, "id.txt")
#         self.names = open(id_file).read().strip().split()
#         assert(len(self.names) == self.nr_of_images)
#         self.name2index = dict(zip(self.names, range(self.nr_of_images)))
#         self.binary_file = os.path.join(datadir, "feature.bin")
#         print ("[%s] %dx%d instances loaded from %s" % (self.__class__.__name__, self.nr_of_images, self.ndims, datadir))
# 
# 
#     def read(self, requested, isname=True):
#         requested = set(requested)
#         if isname:
#             index_name_array = [(self.name2index[x], x) for x in requested if x in self.name2index]
#         else:
#             assert(min(requested)>=0)
#             assert(max(requested)<len(self.names))
#             index_name_array = [(x, self.names[x]) for x in requested]
#         if len(index_name_array) == 0:
#             return [], []
#        
#         index_name_array.sort(key=lambda v:v[0])
#         sorted_index = [x[0] for x in index_name_array]
# 
#         nr_of_images = len(index_name_array)
#         vecs = [None] * nr_of_images
#         offset = np.float32(1).nbytes * self.ndims
#         
#         res = array.array('f')
#         fr = open(self.binary_file, 'rb')
#         fr.seek(index_name_array[0][0] * offset)
#         res.fromfile(fr, self.ndims)
#         previous = index_name_array[0][0]
#  
#         for next in sorted_index[1:]:
#             move = (next-1-previous) * offset
#             #print next, move
#             fr.seek(move, 1)
#             res.fromfile(fr, self.ndims)
#             previous = next
# 
#         fr.close()
# 
#         return [x[1] for x in index_name_array], [ res[i*self.ndims:(i+1)*self.ndims].tolist() for i in range(nr_of_images) ]
# 
# 
#     def read_one(self, name):
#         renamed, vectors = self.read([name])
#         return vectors[0]    
# 
#     def shape(self):
#         return [self.nr_of_images, self.ndims]
# 
# Path: basic/common.py
# ROOT_PATH = "/home/b/VisualSearch1"
# 
# def makedirsforfile(filename):
#     try:
#         os.makedirs(os.path.split(filename)[0])
#     except:
#         pass
# 
# def checkToSkip(filename, overwrite):
#     if os.path.exists(filename):
#         print ("%s exists." % filename),
#         if overwrite:
#             print ("overwrite")
#             return 0
#         else:
#             print ("skip")
#             return 1
#     return 0    


import os
import sys
import timeit
import random
import numpy
import numpy as np
import theano
import theano.tensor as T
import cPickle
from collections import OrderedDict
from theano import sparse
from theano.tensor.shared_randomstreams import RandomStreams
from theano.tensor.nlinalg import diag
from theano.ifelse import ifelse
from simpleknn.bigfile import BigFile
from basic.common import ROOT_PATH, makedirsforfile, checkToSkip
from numpy import linalg as LA
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
    from optparse import OptionParser
            best_validation_loss = this_validation_loss
            best_iter = c_iter

            # write out the parameters of psi modle
            fout_file = os.path.join(model_file, 'psi_lr_%.6f_iter_%d_loss_%.3f.pkl' % ( learning_rate.get_value(), best_iter, best_validation_loss))                  
            fout = file(fout_file, 'wb')
            cPickle.dump(psi, fout, -1)
            fout.close()
        else:
            # do early stopping when loss not increase on validation set on three consecutive times 
            early_stop_counter += 1
            if early_stop_counter > 3:
                break


    end_time = timeit.default_timer()
    print ('The code for file ' +
                          os.path.split(__file__)[1] +
                          ' ran for %.2fm' % ((end_time - start_time) / 60.))




def main(argv=None):
    if argv is None:
        argv = sys.argv[1:]

    parser = OptionParser(usage="""usage: %prog [options] collection""")

    parser.add_option("--overwrite", default=0, type="int", help="overwrite existing file (default: 0)")

# Path: simpleknn/bigfile.py
# class BigFile:
# 
#     def __init__(self, datadir):
#         self.nr_of_images, self.ndims = map(int, open(os.path.join(datadir,'shape.txt')).readline().split())
#         id_file = os.path.join(datadir, "id.txt")
#         self.names = open(id_file).read().strip().split()
#         assert(len(self.names) == self.nr_of_images)
#         self.name2index = dict(zip(self.names, range(self.nr_of_images)))
#         self.binary_file = os.path.join(datadir, "feature.bin")
#         print ("[%s] %dx%d instances loaded from %s" % (self.__class__.__name__, self.nr_of_images, self.ndims, datadir))
# 
# 
#     def read(self, requested, isname=True):
#         requested = set(requested)
#         if isname:
#             index_name_array = [(self.name2index[x], x) for x in requested if x in self.name2index]
#         else:
#             assert(min(requested)>=0)
#             assert(max(requested)<len(self.names))
#             index_name_array = [(x, self.names[x]) for x in requested]
#         if len(index_name_array) == 0:
#             return [], []
#        
#         index_name_array.sort(key=lambda v:v[0])
#         sorted_index = [x[0] for x in index_name_array]
# 
#         nr_of_images = len(index_name_array)
#         vecs = [None] * nr_of_images
#         offset = np.float32(1).nbytes * self.ndims
#         
#         res = array.array('f')
#         fr = open(self.binary_file, 'rb')
#         fr.seek(index_name_array[0][0] * offset)
#         res.fromfile(fr, self.ndims)
#         previous = index_name_array[0][0]
#  
#         for next in sorted_index[1:]:
#             move = (next-1-previous) * offset
#             #print next, move
#             fr.seek(move, 1)
#             res.fromfile(fr, self.ndims)
#             previous = next
# 
#         fr.close()
# 
#         return [x[1] for x in index_name_array], [ res[i*self.ndims:(i+1)*self.ndims].tolist() for i in range(nr_of_images) ]
# 
# 
#     def read_one(self, name):
#         renamed, vectors = self.read([name])
#         return vectors[0]    
# 
#     def shape(self):
#         return [self.nr_of_images, self.ndims]
# 
# Path: basic/common.py
# ROOT_PATH = "/home/b/VisualSearch1"
# 
# def makedirsforfile(filename):
#     try:
#         os.makedirs(os.path.split(filename)[0])
#     except:
#         pass
# 
# def checkToSkip(filename, overwrite):
#     if os.path.exists(filename):
#         print ("%s exists." % filename),
#         if overwrite:
#             print ("overwrite")
#             return 0
#         else:
#             print ("skip")
#             return 1
#     return 0    


# Path: model/psi_model.py
import os
import sys
import timeit
import random
import numpy
import numpy as np
import theano
import theano.tensor as T
import cPickle
from collections import OrderedDict
from theano import sparse
from theano.tensor.shared_randomstreams import RandomStreams
from theano.tensor.nlinalg import diag
from theano.ifelse import ifelse
from simpleknn.bigfile import BigFile
from basic.common import ROOT_PATH, makedirsforfile, checkToSkip
from numpy import linalg as LA
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
    from optparse import OptionParser

            best_validation_loss = this_validation_loss
            best_iter = c_iter

            # write out the parameters of psi modle
            fout_file = os.path.join(model_file, 'psi_lr_%.6f_iter_%d_loss_%.3f.pkl' % ( learning_rate.get_value(), best_iter, best_validation_loss))                  
            fout = file(fout_file, 'wb')
            cPickle.dump(psi, fout, -1)
            fout.close()
        else:
            # do early stopping when loss not increase on validation set on three consecutive times 
            early_stop_counter += 1
            if early_stop_counter > 3:
                break


    end_time = timeit.default_timer()
    print ('The code for file ' +
                          os.path.split(__file__)[1] +
                          ' ran for %.2fm' % ((end_time - start_time) / 60.))




def main(argv=None):
    if argv is None:
        argv = sys.argv[1:]

    parser = OptionParser(usage="""usage: %prog [options] collection""")

    parser.add_option("--overwrite", default=0, type="int", help="overwrite existing file (default: 0)")

    parser.add_option("--rootpath", default=ROOT_PATH, type="string", help="rootpath (default: %s)" % ROOT_PATH)