import current as pc
def model_solver(q):
return q[1] * e**q[0] + 1
#end
coll_points = array([[0, 0, 1], [0, 1, 1]])
#end
basis = pc.basis(0, 1, 2)
print basis
# [1, q0, q1]
#end
solves = model_solver(coll_points)
print solves
# [ 1. 2. 3.71828183]
#end
approx_solver = pc.fitter_lr(basis, coll_points, solves)
print approx_solver
# q1+1.71828182846q0+1.0
#end
print approx_solver(*coll_points)
# [ 1. 2. 3.71828183]
#end
print np.max(abs(approx - approx2).coeffs(), -1)
# [ 2.44249065e-15 3.77475828e-15]
# end
nodes = dist.sample(12, "M")
print nodes
# [[ 1.3074948 1.1461811 0.48907479 -0.99729764 -2.04207273 0.3737412
# -0.17126603 0.40045745 0.25582169 -1.50683751 -0.47799293 1.21890579]
# [ 0.61939522 1.72367491 -0.55533514 -0.00905152 -0.87071076 -0.04532524
# -0.95908033 -1.5433918 -1.10189542 1.19303123 -0.85594892 -0.97358421]]
# end
def model_solver(q):
return [q[0] * q[1], q[0] * e ** -q[1] + 1]
solves = [model_solver(q) for q in nodes.T]
approx = pc.fitter_lr(orth, nodes, solves)
print pc.around(approx, 14)
# [q0q1, 0.161037230451q1^2-1.23004736008q0q1+0.152745901925q0^2+
# 0.0439971157359q1+1.21399892993q0+0.86841679007]
# end
approx = pc.fitter_lr(orth, nodes, solves, rule="OMP", n_nonzero_coefs=1)
print approx
# [q0q1, 1.52536467971q0]
# end
from numpy import *
import current as pc
def model_solver(q):
return q[1]*e**q[0]+1
#end
coll_points = array([[0,0,1],[0,1,1]])
#end
basis = pc.basis(0,1,2)
print basis
# [1, q0, q1]
#end
solves = model_solver(coll_points)
print solves
# [ 1. 2. 3.71828183]
#end
approx_solver = pc.fitter_lr(basis, coll_points, solves)
print approx_solver
# q1+1.71828182846q0+1.0
#end
print approx_solver(*coll_points)
# [ 1. 2. 3.71828183]
#end
E,V = np.empty((2,len(Z)))
N = 4*pc.terms(order, 2)
q = Q.sample(N, "H")
for i in xrange(len(Z)):
print "i", i
z = Z[i]
solver = Solver(z)
trans = lambda q: \
[z/q[0]*(z<q[3]) + \
(q[3]/q[0]+(z-q[3])/q[1])*(z>=q[3])*(z<q[4]) + \
(q[3]/q[0]+(q[4]-q[3])/q[1]+(z-q[4])/q[2])*(z>=q[4]),
q[3]/q[0]+(q[4]-q[3])/q[1]+(1-q[4])/q[2]]
dist = pc.Dist(_length=2)
dist._mom = pc.momgen(trans, 15, Q, rule="C",
composit=[.15,.9,.15, z, z])
orth = pc.orth_chol(order, dist, normed=0)
y = np.array(map(solver, q.T))
approx = pc.fitter_lr(orth, trans(q), y,
rule="T", order=1, alpha=1e-8)
E[i] = pc.E(approx, dist)
V[i] = pc.Var(approx, dist)
E = np.abs(E-E0)
V = np.abs(V-V0)
os.system('echo "%d\n%s\n%s" >> 3a.log' % (N, repr(E), repr(V)))
os.system('echo "%s, %s" >> 3a.log' % (np.mean(E), np.mean(V)))
N = 4 * pc.terms(order, 2)
q = Q.sample(N, "H")
for i in xrange(len(Z)):
print "i", i
z = Z[i]
solver = Solver(z)
trans = lambda q: \
[z/q[0]*(z<q[3]) + \
(q[3]/q[0]+(z-q[3])/q[1])*(z>=q[3])*(z<q[4]) + \
(q[3]/q[0]+(q[4]-q[3])/q[1]+(z-q[4])/q[2])*(z>=q[4]),
q[3]/q[0]+(q[4]-q[3])/q[1]+(1-q[4])/q[2]]
dist = pc.Dist(_length=2)
dist._mom = pc.momgen(trans,
15,
Q,
rule="C",
composit=[.15, .9, .15, z, z])
orth = pc.orth_chol(order, dist, normed=0)
y = np.array(map(solver, q.T))
approx = pc.fitter_lr(orth, trans(q), y, rule="T", order=1, alpha=1e-8)
E[i] = pc.E(approx, dist)
V[i] = pc.Var(approx, dist)
E = np.abs(E - E0)
V = np.abs(V - V0)
os.system('echo "%d\n%s\n%s" >> 3a.log' % (N, repr(E), repr(V)))
os.system('echo "%s, %s" >> 3a.log' % (np.mean(E), np.mean(V)))
