def test_Ising1D():
accuracy = epsilon
J = 0.0
for i in range(5):
nX = np.random.randint(2, high=10)
h = np.random.randn(1)
n = IsingModel1D(nX, h, J, accuracy)
n = mergeContractor(n,
accuracy,
optimize=False,
merge=False,
verbose=0)
assert len(n.nodes) == 1
nn = n.nodes.pop()
assert abs(np.log(nn.tensor.array) / nX -
exactIsing1Dh(h)) < 2 * nX * epsilon
h = 0.0
for i in range(5):
nX = np.random.randint(2, high=10)
J = np.random.randn(1)
n = IsingModel1D(nX, h, J, accuracy)
n = mergeContractor(n,
accuracy,
optimize=False,
merge=False,
verbose=0)
assert len(n.nodes) == 1
nn = n.nodes.pop()
assert abs(np.log(nn.tensor.array) / nX -
exactIsing1DJ(nX, J)) < 2 * nX * epsilon
def isingFreeEnergy(nX, J, k, accuracy):
n = IsingSpinGlass(nX, J, k, accuracy)
n = mergeContractor(n,
accuracy,
heuristic,
optimize=True,
merge=False,
plot=False)
return n.array[1] / (nX)
def ising2DFreeEnergy(nX, nY, h, J, accuracy):
n = IsingModel2Dopen(nX, nY, h, J, accuracy)
n = mergeContractor(n,
accuracy,
heuristic,
optimize=True,
merge=False,
plot=False)
return n.array[1] / (nX * nY)
def ising1DFreeEnergy(nX, h, J, accuracy):
n = IsingModel1Ddisordered(nX, h, J, accuracy)
n = mergeContractor(n,
accuracy,
entropyHeuristic,
optimize=False,
merge=False,
plot=False)
return n.array[1] / nX
def test_Ising2D():
nX = 4
nY = 4
accuracy = 1e-5
h = 0.0
for i in range(5):
J = np.random.randn(1)
n = IsingModel2D(nX, nY, h, J, accuracy)
n = mergeContractor(n, accuracy, optimize=True, merge=True, verbose=0)
assert len(n.nodes) == 1
nn = n.nodes.pop()
assert abs(np.log(nn.tensor.array) /
(nX * nY) - exactIsing2D(J)) < 2 * nX * nY * epsilon + abs(
exactIsing2D(J)) / max(nX, nY)
from TNR.Contractors.mergeContractor import mergeContractor
from TNR.Contractors.heuristics import utilHeuristic as heuristic
from TNR.Utilities.logger import makeLogger
from TNR import config
logger = makeLogger(__name__, config.levels['generic'])
import logging
accuracy = 1e-3
observations = np.random.randint(0, high=20, size=(20, 2))
observations[:, 1] = observations[:, 0] + observations[:, 1]
res = 12
discreteG = np.linspace(0, 1, num=res, endpoint=True)
discreteQ = np.linspace(0, 1, num=res, endpoint=True)
discreteW = np.linspace(0, 1, num=res, endpoint=True)
discreteH = np.linspace(0, 1, num=res, endpoint=True)
n = BayesTest2(observations, discreteG, discreteQ, discreteW, discreteH,
accuracy)
n = mergeContractor(n,
accuracy,
heuristic,
optimize=True,
merge=False,
plot=False)
print(n.nodes.pop().tensor.array)
import numpy as np
from TNR.Models.isingModel import IsingModel2Dopen
from TNR.Contractors.mergeContractor import mergeContractor
fi = open('out.txt', 'w+')
s = 14
nX = s
nY = s
accuracy = 1e-3
h = 0.1
J = 0.5
n = IsingModel2Dopen(nX, nY, h, J, accuracy)
n = mergeContractor(n,
accuracy,
optimize=False,
merge=False,
mergeCut=10,
verbose=2)
assert len(n.nodes) == 1
fi.write(
str(s) + ',' + str(np.log(next(iter(n.nodes)).tensor.array) / s**2) + '\n')
fi.flush()
import numpy as np
from TNR.Models.protein_aggregation_2D import PA2D
from TNR.Contractors.mergeContractor import mergeContractor
nX = 40
nY = 40
accuracy = 1e-5
h = 0.1
J = 0.5
q = 0.1
n = PA2D(nX, nY, h, J, q, accuracy)
n = mergeContractor(n, accuracy, optimize=True, merge=True, verbose=2)
print(len(n.nodes))
for nn in n.nodes:
print(nn.tensor.array)
