Dimension error appears when trying to call minimize function
import numpy as np
import math
import scipy
from scipy import optimize
from scipy.optimize import minimize, line_search
x1=[1,2,1] ; y1=0
x2=[1,1,2] ; y2=0
x3=[2,3,3] ; y3=1
x4=[2,2,1] ; y4=1
x5=[1,2,3] ; y5=0
x6=[1,3,1] ; y6=1
x7=[1,1,1] ; y7=0
x8=[1,2,2] ; y8=0
x9=[1,2,1] ; y9=0
x10=[1,1,1] ; y10=0
x11=[2,2,2] ; y11=1
x12=[1,2,2] ; y12=0
X= np.array([x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12])
y=np.array([y1,y2,y3,y4,y5,y6,y7,y8,y9,y10,y11,y12])
n=len(y)
def h(x):
return 1/(1+np.exp(-x))
def r(beta):
f=0
for i in range(n):
f=f+ (1-y[i])* np.dot(X[i],beta) + np.log( 1+np.exp(-np.dot(X[i],beta) ))
return np.array([f/n])
#gradient of r
def gradr(beta):
f=0
for i in range(n):
mu= h(np.dot(X[i],beta))
f=f+ (mu-y[i])*X[i]
return (f/n).reshape(3,1)
def exactsearch(beta_0,d):
phi_aux = lambda alfa : r(beta_0+ alfa*d)
alfa_0=np.array([1])
bds=[(0,None)]
res = minimize(phi_aux, alfa_0, bounds=bds)
alfa=np.array([res.x])
return alfa
def GradientMethod(beta,f):
N=0
e=10**(-5)
p=-gradr(beta)
alfa=f(beta,p)
while True:
if r(beta)==r(beta+alfa*p):break
if N==10000:break
if alfa<=e:break
else:
N=N+1
beta=beta+alfa*p
p=-gradr(beta)
alfa=f(beta,p)
return [beta,r(beta),N]
GradientMethod(np.array([1,1,1]),exactsearch)
X is a 3 by 12 matrix, r is a function that takes a size 3 vector and operates it with the vectors of X
When changing np.exp to math.exp the error changes to TypeError: only size-1 arrays can be converted to Python scalars. Also, previously I encountered the error ValueError: shapes (3,) and (1,3) not aligned: 3 (dim 0) != 1 (dim 0) but it went away when reshaping gradr.
I must add that I don't understand that much the function exactsearch, since it was given to me.
Full error
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-20-bb9e6dc26271> in <module>
62 return [beta,r(beta),N]
63
---> 64 GradientMethod(np.array([1,1,1]),exactsearch)
<ipython-input-20-bb9e6dc26271> in GradientMethod(beta, f)
50 e=10**(-5)
51 p=-gradr(beta)
---> 52 alfa=f(beta,p)
53 while True:
54 if r(beta)==r(beta+alfa*p):break
<ipython-input-20-bb9e6dc26271> in exactsearch(beta_0, d)
42 alfa_0=np.array([1])
43 bds=[(0,None)]
---> 44 res = minimize(phi_aux, alfa_0, bounds=bds)
45 alfa=np.array([res.x])
46 return alfa
~/anaconda3/lib/python3.8/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
615 **options)
616 elif meth == 'l-bfgs-b':
--> 617 return _minimize_lbfgsb(fun, x0, args, jac, bounds,
618 callback=callback, **options)
619 elif meth == 'tnc':
~/anaconda3/lib/python3.8/site-packages/scipy/optimize/lbfgsb.py in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)
304 iprint = disp
305
--> 306 sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
307 bounds=new_bounds,
308 finite_diff_rel_step=finite_diff_rel_step)
~/anaconda3/lib/python3.8/site-packages/scipy/optimize/optimize.py in _prepare_scalar_function(fun, x0, jac, args, bounds, epsilon, finite_diff_rel_step, hess)
259 # ScalarFunction caches. Reuse of fun(x) during grad
260 # calculation reduces overall function evaluations.
--> 261 sf = ScalarFunction(fun, x0, args, grad, hess,
262 finite_diff_rel_step, bounds, epsilon=epsilon)
263
~/anaconda3/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py in __init__(self, fun, x0, args, grad, hess, finite_diff_rel_step, finite_diff_bounds, epsilon)
93
94 self._update_grad_impl = update_grad
---> 95 self._update_grad()
96
97 # Hessian Evaluation
~/anaconda3/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py in _update_grad(self)
169 def _update_grad(self):
170 if not self.g_updated:
--> 171 self._update_grad_impl()
172 self.g_updated = True
173
~/anaconda3/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py in update_grad()
89 self._update_fun()
90 self.ngev += 1
---> 91 self.g = approx_derivative(fun_wrapped, self.x, f0=self.f,
92 **finite_diff_options)
93
~/anaconda3/lib/python3.8/site-packages/scipy/optimize/_numdiff.py in approx_derivative(fun, x0, method, rel_step, abs_step, f0, bounds, sparsity, as_linear_operator, args, kwargs)
386 f0 = np.atleast_1d(f0)
387 if f0.ndim > 1:
--> 388 raise ValueError("`f0` passed has more than 1 dimension.")
389
390 if np.any((x0 < lb) | (x0 > ub)):
ValueError: `f0` passed has more than 1 dimension.
