Dmitry Barskov Dimanaux

## least_squares.py
"""
Basic Least Squares Implementation with pseudo inverse
"""
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(0)

t = np.linspace(0, 3, 20)  # horizontal axis
x = np.array([3, 0, 4])  # a,b,c  of f(t)=a+b*t+c*t^2
f = lambda t, x: x[0]+x[1]*t+x[2]*t**2  # f(t)
	"""
	Basic Least Squares Implementation with pseudo inverse
	"""
	import numpy as np
	import matplotlib.pyplot as plt
	np.random.seed(0)

	t = np.linspace(0, 3, 20) # horizontal axis
	x = np.array([3, 0, 4]) # a,b,c of f(t)=a+bt+ct^2
	f = lambda t, x: x[0]+x[1]t+x[2]t**2 # f(t)