<s> __author__ = '<STR_LIT>' <EOL> from learnpy . models . Model import Model <EOL> import pandas as pd <EOL> from learnpy . models . ModelUtil import * <EOL> import operator <EOL> import time <EOL> class NeuralNetwork ( Model ) : <EOL> def __init__ ( self , data , class_column , the_num_iter ) : <EOL> """<STR_LIT>""" <EOL> print ( "<STR_LIT>" ) <EOL> print ( "<STR_LIT>" , the_num_iter , "<STR_LIT:\n>" ) <EOL> self . num_iter = the_num_iter <EOL> self . predict_summary = { } <EOL> self . fit_report = { } <EOL> self . data = data <EOL> self . class_column = class_column <EOL> col_data = self . data [ class_column ] <EOL> self . class_list = unique_list ( col_data ) <EOL> self . class_func = { self . class_list [ <NUM_LIT:0> ] : <NUM_LIT:0> , self . class_list [ <NUM_LIT:1> ] : <NUM_LIT:1> } <EOL> self . num_cols , self . cat_cols = get_both_columns ( self . data , class_column ) <EOL> self . data = numericalize_data ( self . data , self . num_cols , self . class_column , self . class_func ) <EOL> self . stats_info = stats_info ( self . data , self . num_cols , self . class_column , self . class_func ) <EOL> self . syn0 = None <EOL> self . data = normalize_data ( self . data ) <EOL> info_print ( "<STR_LIT>" ) <EOL> def fit ( self , data ) : <EOL> """<STR_LIT>""" <EOL> if data is None : <EOL> self . train_self ( ) <EOL> else : <EOL> pass <EOL> def train_self ( self ) : <EOL> """<STR_LIT>""" <EOL> feature_set = self . data [ self . num_cols ] . as_matrix ( ) <EOL> label_array = self . data [ [ self . class_column ] ] . as_matrix ( ) <EOL> np . random . seed ( <NUM_LIT> ) <EOL> syn0 = <NUM_LIT:2> * np . random . random ( ( feature_set . shape [ <NUM_LIT:1> ] , <NUM_LIT:1> ) ) - <NUM_LIT:1> <EOL> for i in range ( <NUM_LIT:0> , self . num_iter ) : <EOL> l0 = feature_set <EOL> l1 = nonlin ( np . dot ( l0 , syn0 ) ) <EOL> l1_error = label_array - l1 <EOL> l1_delta = l1_error * nonlin ( l1 , True ) <EOL> syn0 += np . dot ( l0 . T , l1_delta ) <EOL> self . syn0 = syn0 <EOL> output = list ( pd . DataFrame ( l1 ) [ <NUM_LIT:0> ] . map ( lambda x : get_class ( x ) ) ) <EOL> target = list ( pd . DataFrame ( label_array ) [ <NUM_LIT:0> ] ) <EOL> self . fit_report [ '<STR_LIT>' ] = output <EOL> self . fit_report [ '<STR_LIT:target>' ] = target <EOL> print ( "<STR_LIT>" ) <EOL> self . report ( ) <EOL> def predict ( self , testing_data ) : <EOL> """<STR_LIT>""" <EOL> testing_data = transform_data ( testing_data , self . num_cols , self . class_column , self . stats_info , self . class_func ) <EOL> feature_set = testing_data [ self . num_cols ] . as_matrix ( ) <EOL> label_array = testing_data [ [ self . class_column ] ] . as_matrix ( ) <EOL> l1 = nonlin ( np . dot ( feature_set , self . syn0 ) ) <EOL> output = list ( pd . DataFrame ( l1 ) [ <NUM_LIT:0> ] . map ( lambda x : get_class ( x ) ) ) <EOL> target = list ( pd . DataFrame ( label_array ) [ <NUM_LIT:0> ] ) <EOL> self . predict_summary [ '<STR_LIT>' ] = output <EOL> self . predict_summary [ '<STR_LIT:target>' ] = target <EOL> print ( "<STR_LIT>" ) <EOL> self . summary ( ) <EOL> def summary ( self ) : <EOL> """<STR_LIT>""" <EOL> print ( "<STR_LIT>" , calculate_acc ( self . predict_summary [ '<STR_LIT>' ] , self . predict_summary [ '<STR_LIT:target>' ] ) ) <EOL> def report ( self ) : <EOL> """<STR_LIT>""" <EOL> print ( "<STR_LIT>" , calculate_acc ( self . fit_report [ '<STR_LIT>' ] , self . fit_report [ '<STR_LIT:target>' ] ) ) </s>
