solution.py

@@ -1,42 +0,0 @@
-import numpy
-import pandas
-import sys
-from sklearn.linear_model import LinearRegression
-
-TRAIN_URL = "train/train.tsv"
-TEST_A = "test-A"
-DEV_0 = "dev-0"
-
-def get_trained_model(data):
-    x = numpy.array(data.get('x')).reshape(-1, 1)
-    y = numpy.array(data.get('y')).reshape(-1, 1)
-    m = LinearRegression()
-    m.fit(x, y)
-    return m
-
-
-def prepare_model():
-    csv = pandas.read_csv(TRAIN_URL, sep='\t', header=None, index_col=None)
-    data = {'x': csv[2].tolist(), 'y': csv[0].tolist()}
-    model = get_trained_model(data)
-    return model
-
-
-def calculate_prediction(path, prepared_model):
-    data_frame = pandas.read_csv(path + '/in.tsv', sep='\t', header=None, index_col=None)
-    input_array = numpy.array(data_frame[1].tolist()).reshape(-1, 1)
-    prediction = prepared_model.predict(input_array)
-    numpy.savetxt(path + "/out.tsv", prediction, fmt="%d", delimiter='\n')
-
-
-def predict_dev_zero(prepared_model):
-    calculate_prediction(DEV_0, prepared_model)
-
-
-def predict_test_a(prepared_model):
-    calculate_prediction(TEST_A, prepared_model)
-
-
-prepared_model = prepare_model()
-predict_dev_zero(prepared_model)
-predict_test_a(prepared_model)