skrypt-dev-0.py

@@ -1,43 +0,0 @@
-import numpy as np
-import pandas as pd
-from scipy.sparse import data
-from sklearn import linear_model
-from sklearn import preprocessing
-from sklearn.pipeline import make_pipeline
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn import linear_model
-import csv
-import pandas as pd
-
-regression = linear_model.LinearRegression()  
-    
-train_file = pd.read_csv('train/train.tsv', delimiter='\t', names=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
-train_data_frame = pd.DataFrame(train_file, columns=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
-
-Y = train_data_frame[['price']]
-X = train_data_frame[['year', 'mileage', 'engineCapacity']]
-
-regression.fit(X, Y)
-
-in_file = pd.read_csv('dev-0/in.tsv', delimiter='\t', names=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
-in_data_frame = pd.DataFrame(in_file, columns=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
-
-reshape = in_data_frame[['year', 'mileage', 'engineCapacity']]
-
-y_predict = regression.predict(reshape)
-y_predict = np.concatenate(y_predict)
-
-
-labels = np.array2string(y_predict, separator='\n', suppress_small=True)
-        
-file_out = open("dev-0/out.tsv", 'w')
-file_out.write(labels[1:-1])
-
-with open("dev-0/out.tsv", 'r') as fix_space:
-    lines = fix_space.readlines()
-
-    lines = [line.replace(' ', '') for line in lines]
-    lines = [line.replace('[', '') for line in lines]
-    lines = [line.replace(']', '') for line in lines]
-with open("dev-0/out.tsv", 'w') as fix_space:
-    fix_space.writelines(lines)

skrypt-test-a.py

@@ -1,41 +0,0 @@
-import numpy as np
-import pandas as pd
-from scipy.sparse import data
-from sklearn import linear_model
-from sklearn import preprocessing
-from sklearn.pipeline import make_pipeline
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn import linear_model
-import csv
-import pandas as pd
-
-regression = linear_model.LinearRegression()  
-    
-train_file = pd.read_csv('train/train.tsv', delimiter='\t', names=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
-train_data_frame = pd.DataFrame(train_file, columns=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
-
-Y = train_data_frame[['price']]
-X = train_data_frame[['year', 'mileage', 'engineCapacity']]
-
-regression.fit(X, Y)
-
-in_file = pd.read_csv('test-A/in.tsv', delimiter='\t', names=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
-in_data_frame = pd.DataFrame(in_file, columns=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
-
-reshape = in_data_frame[['year', 'mileage', 'engineCapacity']]
-
-y_predict = regression.predict(reshape)
-y_predict = np.concatenate(y_predict)
-
-
-labels = np.array2string(y_predict, separator='\n', suppress_small=True)
-        
-file_out = open("test-A/out.tsv", 'w')
-file_out.write(labels[1:-1])
-
-with open("test-A/out.tsv", 'r') as fix_space:
-    lines = fix_space.readlines()
-
-    lines = [line.replace(' ', '') for line in lines]
-with open("test-A/out.tsv", 'w') as fix_space:
-    fix_space.writelines(lines)