KONE

import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from sklearn.svm import OneClassSVM

df = pd.read_csv('Friday.csv') # monday data
df["date"] = np.array(range(len(df["date"])))
df.head()

kmeans = KMeans(init='k-means++', n_clusters=3, n_init=7, precompute_distances=True, algorithm='elkan')
preds = kmeans.fit_predict(df)

date = df["date"]
vals = df["vals"]
for i in range(3):
    plt.scatter(date[preds == i], vals[preds == i])
plt.show()


clf = OneClassSVM(nu=0.1, kernel="rbf", gamma=0.001, verbose=True)
for i in range(3):
    date_one = df["date"][preds == i]
    vals_one = df["vals"][preds == i]
    data = np.array([vals_one]).reshape(-1,1)
    clf.fit(data)
    one_preds = clf.predict(data)
    plt.scatter(date_one[one_preds == 1], vals_one[one_preds == 1], c="black")
    plt.scatter(date_one[one_preds == -1], vals_one[one_preds == -1], c="red")
plt.legend(handles=[mpatches.Patch(color='black', label='Normal'), mpatches.Patch(color='red', label='Not Normal')])
plt.xlabel("Dates")
plt.ylabel("Values")
plt.savefig('friday.png')
plt.show()