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simplify S before printing it

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neingeist 2014-04-21 11:28:51 +02:00
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commit d0faaddc91
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least-squares.py
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@ -1,6 +1,6 @@
#!/usr/bin/python2.7 #!/usr/bin/python2.7
from __future__ import division, print_function from __future__ import division, print_function
from sympy import Symbol, diff, solve, lambdify from sympy import Symbol, diff, solve, lambdify, simplify
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
@ -16,6 +16,7 @@ data = [(1,14), (2, 13), (3, 12), (4, 10), (5,9), (7,8), (9,5)]
# For each data point the vertical error/residual is x*b1 + b2 - y. We want to # For each data point the vertical error/residual is x*b1 + b2 - y. We want to
# minimize the sum of the squared residuals (least squares). # minimize the sum of the squared residuals (least squares).
S = sum((p[0] * b1 + b2 - p[1]) ** 2 for p in data) S = sum((p[0] * b1 + b2 - p[1]) ** 2 for p in data)
S = simplify(S)
print("Function to minimize: S = {}".format(S)) print("Function to minimize: S = {}".format(S))
# Minimize S by setting its partial derivatives to zero. # Minimize S by setting its partial derivatives to zero.