from provider import Provider
from feature_selection import Selector
from dimension_reduction import Reducer
from sklearn import tree
from sklearn.metrics import mean_squared_error
from math import sqrt
import matplotlib.pyplot as plt
if __name__ == '__main__':
provider = Selector()
input = []
target = []
for d in provider.getLearnData():
input.append(d[0])
target.append(d[1][0])
regressor = tree.DecisionTreeRegressor()
regressor.fit(input, target)
mult = 1 #provider.multiplier
results = regressor.predict(input)
print(results)
w = open('output4_1_sr.txt', 'w')
for i, line in enumerate(results):
w.write('%f;%f\n' % (target[i] * mult, line * mult))
w.close()
input = []
target = []
etrue = []
epredict = []
from feature_selection import Selector
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from math import sqrt
import matplotlib.pyplot as plt
if __name__ == '__main__':
# reducer = Reducer()
reducer = Selector()
input = []
target = []
for d in reducer.getLearnData():
input.append(d[0])
target.append(d[1][0])
regressor = LinearRegression()
regressor.fit(input, target)
print(regressor.coef_)
mult = 1 #reducer.provider.multiplier
results = regressor.predict(input)
print(results)
w = open('output2_1_s.txt', 'w')
for i, line in enumerate(results):
w.write('%f;%f\n' % (target[i] * mult, line * mult))
w.close()
input = []
target = []
for d in reducer.getTestData():