from sklearn.linear_model import LogisticRegression

import numpy as np

data = np.loadtxt("output.txt", delimiter=',')

data = data[7:,]

x = data[:,6]

lists = []
for xx in x:
    tmp = []
    tmp.append(xx)
    lists.append(tmp)

y = data[:,4]

clf = LogisticRegression(C=1e5)

clf.fit(lists ,y)

print (np.exp(clf.intercept_),np.exp(clf.coef_.ravel()))

def lr_model(clf,x):
    return 1/ ( 1+np.exp(-(clf.intercept_ + clf.coef_ * x)))

import matplotlib.pyplot as plt

plt.clf()

plt.scatter(lists, y, color='black', zorder=20)

plt.clf()
plt.scatter(lists, y, color='black', zorder=20)
X_test = np.linspace(1000, 1030, 100)


def model(x):
    return 1 / (1 + np.exp(-x))
loss = model(X_test * clf.coef_ + clf.intercept_).ravel()
plt.plot(X_test, loss, color='blue', linewidth=3)

lr_model(clf,1020)

clf.predict_proba(1020)