From 5ecbd135cef75ae5aa19080e365c7dabde17d9e8 Mon Sep 17 00:00:00 2001
From: John McMaster <johndmcmaster@gmail.com>
Date: Wed, 29 Aug 2018 14:05:48 -0700
Subject: [PATCH] timfuz leastsq: remove test code

Signed-off-by: John McMaster <johndmcmaster@gmail.com>
---
 timfuz/solve_leastsq.py | 161 ----------------------------------------
 1 file changed, 161 deletions(-)

diff --git a/timfuz/solve_leastsq.py b/timfuz/solve_leastsq.py
index 6d298969..fde102c2 100644
--- a/timfuz/solve_leastsq.py
+++ b/timfuz/solve_leastsq.py
@@ -175,166 +175,5 @@ def main():
     finally:
         print('Exiting after %s' % bench)
 
-# optimize.curve_fit wrapper
-def test1():
-    # Generate artificial data = straight line with a=0 and b=1
-    # plus some noise.
-    xdata = np.array([0.0,1.0,2.0,3.0,4.0,5.0])
-    ydata = np.array([0.1,0.9,2.2,2.8,3.9,5.1])
-    # Initial guess.
-    x0    = np.array([0.0, 0.0, 0.0])
-    sigma = np.array([1.0,1.0,1.0,1.0,1.0,1.0])
-
-    def func(x, a, b, c):
-        return a + b*x + c*x*x
-
-    print(optimize.curve_fit(func, xdata, ydata, x0, sigma))
-
-# optimize.leastsq
-def test2():
-
-    # The function whose square is to be minimised.
-    # params ... list of parameters tuned to minimise function.
-    # Further arguments:
-    # xdata ... design matrix for a linear model.
-    # ydata ... observed data.
-    def func(params, xdata, ydata):
-        return (ydata - np.dot(xdata, params))
-
-    x0 = np.array([0.0, 0.0])
-
-    '''
-    a  = 10
-    a + b = 100
-    '''
-    xdata = np.array([[1, 0],
-                      [1, 1]])
-    ydata = np.array([10, 100])
-
-    '''
-    x [ 10.  90.]
-    cov_x [[ 1. -1.]
-     [-1.  2.]]
-    infodictx {'ipvt': array([1, 2], dtype=int32), 'qtf': array([  1.69649118e-10,   1.38802454e-10]), 'nfev': 7, 'fjac': array([[ 1.41421356,  0.70710678],
-           [ 0.70710678,  0.70710678]]), 'fvec': array([ 0.,  0.])}
-    mesg The relative error between two consecutive iterates is at most 0.000000
-    ier 2
-      Solution found: True
-    '''
-    x, cov_x, infodict, mesg, ier = optimize.leastsq(func, x0, args=(xdata, ydata), full_output=True)
-    print('x', x)
-    print('cov_x', cov_x)
-    print('infodictx', infodict)
-    print('mesg', mesg)
-    print('ier', ier)
-    print('  Solution found: %s' % (ier in (1, 2, 3, 4)))
-
-# non-square
-def test3():
-    def func(params, xdata, ydata):
-        return (ydata - np.dot(xdata, params))
-
-    x0 = np.array([0.0, 0.0, 0.0])
-
-    '''
-    a  = 10
-    a + b + c = 100
-    '''
-    xdata = np.array([[1, 0, 0],
-                      [1, 1, 1],
-                      [0, 0, 0]])
-    ydata = np.array([10, 100, 0])
-
-    x, cov_x, infodict, mesg, ier = optimize.leastsq(func, x0, args=(xdata, ydata), full_output=True)
-    print('x', x)
-    print('cov_x', cov_x)
-    print('infodictx', infodict)
-    print('mesg', mesg)
-    print('ier', ier)
-    print('  Solution found: %s' % (ier in (1, 2, 3, 4)))
-
-def test4():
-    def func(params):
-        print('')
-        print('iter')
-        print(params)
-        print(xdata)
-        print(ydata)
-        return (ydata - np.dot(xdata, params))
-
-    x0 = np.array([0.0, 0.0, 0.0])
-
-    '''
-    You must have at least as many things to optimize as variables
-    That is, the system must be plausibly constrained for it to attempt a solve
-    If not, you'll get a message like
-    TypeError: Improper input: N=3 must not exceed M=2
-    '''
-    xdata = np.array([[1, 0, 0],
-                      [1, 1, 1],
-                      [1, 0, 1],
-                      [0, 1, 1],
-                      ])
-    ydata = np.array([10, 100, 120, 140])
-
-    x, cov_x, infodict, mesg, ier = optimize.leastsq(func, x0, full_output=True)
-    print('x', x)
-    print('cov_x', cov_x)
-    print('infodictx', infodict)
-    print('mesg', mesg)
-    print('ier', ier)
-    print('  Solution found: %s' % (ier in (1, 2, 3, 4)))
-
-def test5():
-    from scipy.optimize import least_squares
-
-    def fun_rosenbrock(x):
-        return np.array([10 * (x[1] - x[0]**2), (1 - x[0])])
-    x0_rosenbrock = np.array([2, 2])
-    res = least_squares(fun_rosenbrock, x0_rosenbrock)
-    '''
-     active_mask: array([ 0.,  0.])
-            cost: 9.8669242910846867e-30
-             fun: array([  4.44089210e-15,   1.11022302e-16])
-            grad: array([ -8.89288649e-14,   4.44089210e-14])
-             jac: array([[-20.00000015,  10.        ],
-           [ -1.        ,   0.        ]])
-         message: '`gtol` termination condition is satisfied.'
-            nfev: 3
-            njev: 3
-      optimality: 8.8928864934219529e-14
-          status: 1
-         success: True
-               x: array([ 1.,  1.])
-    '''
-    print(res)
-
-def test6():
-    def func(params):
-        return (ydata - np.dot(xdata, params))
-
-    x0 = np.array([0.0, 0.0, 0.0])
-
-    '''
-    a  = 10
-    a + b + c = 100
-    '''
-    xdata = np.array([[1, 0, 0],
-                      [1, 1, 1],
-                      [0, 0, 0]])
-    ydata = np.array([10, 100, 0])
-
-    res = least_squares(func, x0)
-    '''
-    '''
-    print(res)
-
 if __name__ == '__main__':
     main()
-    #test1()
-    #test2()
-    #test3()
-    #test4()
-    #test5()
-    #test6()
-