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#include <stdio.h>
#include <cs50.h>

int main(void)
{
  //declare function to check validity of right angled triangle
  bool valid_righttriangle(float x, float y, float z)
  {
   //to check using Pythagoras' Theorem
  if (x*x + y*y = z*z) || (x*x + z*z = y*y) || (y*y + z*z = x*x)

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# Import library to use the function 'random.randint()'.
import random

# User-defined function to print list details.
def list_details(l_length, l_bound, u_bound):
  # Create an empty list to load values in.
  v_list = []

  # Use a for-loop to append random integers into the list.
  for i in range(l_length):

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# Open the file "word_list.txt" as "word_file" for reading.
word_file = open("word_list.txt", "r")

# Use the read() function to store the contents of the file to word_list.
word_list = word_file.read()

# word_list is currently a string of all characters inside word_list.txt.
# We need to separate the characters into individual words and store it in an array.
# Because each word is separated by a newline character '\n', we use the split() function.
# Now, individual words can be accessed from the array 'word_array'.

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from statsmodels.tsa.holtwinters import ExponentialSmoothing

fit = ExponentialSmoothing(data, seasonal_periods=periodicity, trend='add', seasonal='add').fit(use_boxcox=True)
fit.fittedvalues.plot(color='blue')
fit.forecast(5).plot(color='green')
plt.show()

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from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

p = 5  # lag
d = 1  # difference order
q = 0  # size of moving average window

train, test = train_test_split(X, test_size=0.20, shuffle=False)
history = train.tolist()

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from sklearn.cluster import KMeans

clusters = 3
y_pred = KMeans(n_clusters=clusters).fit_predict(X)

plt.scatter(X[:,0], X[:,1], c=y_pred)
plt.show()

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from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, shuffle=True)

clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

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from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, shuffle=True)

clf = SVC(gamma='auto')
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

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from kenchi.outlier_detection.statistical import HBOS

hbos = HBOS(novelty=True).fit(X)
y_pred = hbos.predict(X)

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from sklearn.cluster import AgglomerativeClustering

clusters = 3
y_pred = AgglomerativeClustering(n_clusters=clusters).fit_predict(X)


from scipy.cluster.hierarchy import linkage, fcluster, dendrogram

clusters=5
cls = linkage(X, method='ward')