Create estimation.py

utilities/estimation.py

@@ -0,0 +1,65 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def ESTIMATION_1(X,Y):
+	try:
+		assert (len(X) == len(Y))
+	except:
+		print ('Size of arrays are not the same. Quitting .... ')
+		exit(0)
+
+	n = len(X)
+	sum_Y = sum(Y)
+	sum_X = sum(X)
+
+	A = np.matrix([[n]])
+	B = np.matrix([[sum_Y]])
+	A_inverse = np.linalg.inv(A)
+	# Calculating the coefficients of least square
+	min_square = A_inverse*B
+	return (min_square)
+
+
+def ESTIMATION_2(X,Y):
+	try:
+		assert (len(X) == len(Y))
+	except:
+		print ('Size of arrays are not the same. Quitting .... ')
+		exit(0)
+
+	n = len(X)
+	sum_Y = sum(Y)
+	sum_X = sum(X)
+	sum_X2 = sum([x*x for x in X])
+	sum_XY = sum([x*y for x,y in zip(X,Y)])
+
+	A2 = np.matrix([[n, sum_X],[sum_X,sum_X2]])
+	B2 = np.matrix([[sum_Y],[sum_XY]])
+	A2_inverse = np.linalg.inv(A2)
+	# Calculating the coefficients of least square
+	min_square2 = A2_inverse*B2
+	return (min_square2)
+
+def ESTIMATION_3(X,Y):
+	try:
+		assert (len(X) == len(Y))
+	except:
+		print ('Size of arrays are not the same. Quitting .... ')
+		exit(0)
+
+	n = len(X)
+	sum_Y = sum(Y)
+	sum_X = sum(X)
+	sum_X2 = sum([x*x for x in X])
+	sum_X3 = sum([x*x*x for x in X])
+	sum_X4 = sum([x*x*x*x for x in X])
+	sum_XY = sum([x*y for x,y in zip(X,Y)])
+	sum_X2Y = sum([x*x*y for x,y in zip(X,Y)])
+
+	A3 = np.matrix([[n, sum_X, sum_X2],[sum_X,sum_X2, sum_X3],[sum_X2,sum_X3, sum_X4]])
+	B3 = np.matrix([[sum_Y],[sum_XY],[sum_X2Y]])
+	A3_inverse = np.linalg.inv(A3)
+
+	# Calculating the coefficients of least square
+	min_square3 = A3_inverse*B3
+	return (min_square3)