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 = []
for d in provider.getTestData():
input.append(d[0])
target.append(d[1][0])
results = regressor.predict(input)
w = open('output4_2_sr.txt', 'w')
for i, line in enumerate(results):
w.write('%f;%f\n' % (target[i] * mult, line * mult))
w.close()
w = open('rt_mse.txt', 'w')
w.write('%f;\n' % sqrt(mean_squared_error(target, results)))
w.close()
plt.plot(target, 'b', results, 'r')
plt.ylabel('Reikšmė')
plt.xlabel('Masyvo elementas')
plt.title('Regression tree grafikas')
plt.show()
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():
input.append(d[0])
target.append(d[1][0])
results = regressor.predict(input)
w = open('output2_2_s.txt', 'w')
for i, line in enumerate(results):
w.write('%f;%f\n' % (target[i] * mult, line * mult))
w.close()
class Linear:
def __init__(self, provider):
self.provider = provider
input = []
target = []
for d in self.provider.getLearnData():