aihubprojects

    k_means = K_Means(K)
    k_means.fit(data)
    
    
    # Plotting starts here
    colors = 10*["r", "g", "c", "b", "k"]

    for centroid in k_means.centroids:
        plt.scatter(k_means.centroids[centroid][0], k_means.centroids[centroid][1], s = 130, marker = "x")

aihubprojects

    #generate dummy cluster datasets
    # Set three centers, the model should predict similar results
    center_1 = np.array([1,1])
    center_2 = np.array([5,5])
    center_3 = np.array([8,1])

    # Generate random data and center it to the three centers
    cluster_1 = np.random.randn(100, 2) + center_1
    cluster_2 = np.random.randn(100,2) + center_2
    cluster_3 = np.random.randn(100,2) + center_3

aihubprojects

            previous = dict(self.centroids)
            for cluster_index in self.classes:
                self.centroids[cluster_index] = np.average(self.classes[cluster_index], axis = 0)
                
            isOptimal = True
            
            for centroid in self.centroids:
                original_centroid = previous[centroid]
                curr = self.centroids[centroid]
                if np.sum((curr - original_centroid)/original_centroid * 100.0) > self.tolerance:

aihubprojects

       for i in range(self.max_iterations):
            
            self.classes = {}
            for j in range(self.k):
                self.classes[j] = []
                
            
            for point in data:
                distances = []
                for index in self.centroids:

aihubprojects

    def fit(self, data):
        self.centroids = {}
        for i in range(self.k):
            self.centroids[i] = data[i]

aihubprojects

    def euclidean_distance(self, point1, point2):
            #return math.sqrt((point1[0]-point2[0])**2 + (point1[1]-point2[1])**2 + (point1[2]-point2[2])**2)   #sqrt((x1-x2)^2 + (y1-y2)^2)
            return np.linalg.norm(point1-point2, axis=0)

aihubprojects

import pandas as pd
import numpy as np
import random as rd
import matplotlib.pyplot as plt
import math

class K_Means:
    
    def __init__(self, k=2, tolerance = 0.001, max_iter = 500):
        self.k = k

aihubprojects

x_fut = np.arange(30).reshape(-1,1)
xf = x_fut+x1[-1:]
y_fut = (model.predict(polyfet.transform(xf))).astype(int)

plt.figure(figsize=(16, 10)) 
plt.plot(x1,yp,"--b")
plt.plot(x1,yact,"-g")
plt.plot(xf,y_fut,"--r")
plt.legend(['predicted', 'actual',"future_pred"])
plt.xticks()

aihubprojects

polyfet = PolynomialFeatures(degree=4) #you can change degree
xa = polyfet.fit_transform(x1)
model = linear_model.LinearRegression()
model.fit(xa,y)
yp = model.predict(xa)
yact = np.array(df['total_cases'])#.reshape(-1,1)

plt.figure(figsize=(8, 6)) 
plt.plot(yp,"--b")
plt.plot(yact,"-g")

aihubprojects

from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model
from sklearn.linear_model import LinearRegression

print('--'*15,end ='');print('polynomial model training',end ='');print('--'*10)

for i in range(1,6):
    polyfet = PolynomialFeatures(degree=i)
    xa = polyfet.fit_transform(x1)
    model = linear_model.LinearRegression()