<s> import numpy as np <EOL> import matplotlib . pyplot as plt <EOL> from sklearn import svm , preprocessing <EOL> import pandas as pd <EOL> from matplotlib import style <EOL> style . use ( "<STR_LIT>" ) <EOL> FEATURES = [ <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' <EOL> ] <EOL> def Build_Data_Set ( ) : <EOL> data_df = pd . DataFrame . from_csv ( "<STR_LIT>" ) <EOL> data_df = data_df . reindex ( np . random . permutation ( data_df . index ) ) <EOL> X = np . array ( data_df [ FEATURES ] . values ) <EOL> y = ( data_df [ "<STR_LIT>" ] <EOL> . replace ( "<STR_LIT>" , <NUM_LIT:0> ) <EOL> . replace ( "<STR_LIT>" , <NUM_LIT:1> ) <EOL> . values . tolist ( ) <EOL> ) <EOL> X = preprocessing . scale ( X ) <EOL> return X , y <EOL> def Analysis ( ) : <EOL> test_size = <NUM_LIT:1000> <EOL> X , y = Build_Data_Set ( ) <EOL> print ( len ( X ) ) <EOL> clf = svm . SVC ( kernel = "<STR_LIT>" , C = <NUM_LIT:1.0> ) <EOL> clf . fit ( X [ : - test_size ] , y [ : - test_size ] ) <EOL> correct_count = <NUM_LIT:0> <EOL> for x in range ( <NUM_LIT:1> , test_size + <NUM_LIT:1> ) : <EOL> if clf . predict ( X [ - x ] ) [ <NUM_LIT:0> ] == y [ - x ] : <EOL> correct_count += <NUM_LIT:1> <EOL> print ( "<STR_LIT>" % float ( correct_count ) ) <EOL> print ( "<STR_LIT>" % float ( test_size ) ) <EOL> print ( "<STR_LIT>" , ( float ( correct_count ) / float ( test_size ) ) * <NUM_LIT> ) <EOL> Analysis ( ) </s>
