def tst_robust_glm():
from scitbx.glmtbx import robust_glm
from scitbx.array_family import flex
from numpy.random import poisson, seed
from math import exp
seed(0)
n_obs = 100
for c in range(1, 100):
# Test for a constant value
X = flex.double([1 for i in range(n_obs)])
X.reshape(flex.grid(n_obs, 1))
Y = flex.double(list(poisson(c, n_obs)))
B = flex.double([0])
result = robust_glm(X, Y, B, family="poisson", max_iter=100)
assert (abs(c - exp(result.parameters()[0])) < 0.1 * c)
# Now test with a massive outlier
for c in range(1, 100):
# Test for a constant value
X = flex.double([1 for i in range(n_obs)])
X.reshape(flex.grid(n_obs, 1))
Y = flex.double(list(poisson(c, n_obs)))
Y[n_obs // 2] = c * 100
B = flex.double([0])
result = robust_glm(X, Y, B, family="poisson", max_iter=100)
assert (abs(c - exp(result.parameters()[0])) < 0.1 * c)
print('OK')
def tst_robust_glm():
from scitbx.glmtbx import robust_glm
from scitbx.array_family import flex
from numpy.random import poisson, seed
from math import exp
seed(0)
n_obs = 100
for c in range(1, 100):
# Test for a constant value
X = flex.double([1 for i in range(n_obs)])
X.reshape(flex.grid(n_obs, 1))
Y = flex.double(list(poisson(c, n_obs)))
B = flex.double([0])
result = robust_glm(X, Y, B, family="poisson", max_iter=100)
assert(abs(c - exp(result.parameters()[0])) < 0.1*c)
# Now test with a massive outlier
for c in range(1, 100):
# Test for a constant value
X = flex.double([1 for i in range(n_obs)])
X.reshape(flex.grid(n_obs, 1))
Y = flex.double(list(poisson(c, n_obs)))
Y[n_obs//2] = c * 100
B = flex.double([0])
result = robust_glm(X, Y, B, family="poisson", max_iter=100)
assert(abs(c - exp(result.parameters()[0])) < 0.1*c)
print 'OK'
0,
4,
4,
3,
0,
1,
4,
1,
1,
1,
6]
# a[4] = 10
# a[50] = 100
X = flex.double([1] * len(a))
X.reshape(flex.grid(len(a), 1))
Y = flex.double(a)
B = flex.double([0])
result = robust_glm(X, Y, B, family="poisson")
print list(result.parameters())
from matplotlib import pylab
print "MOM1: ", sum(means1) / len(means1)
print "MOM2: ", sum(means2) / len(means2)
pylab.plot(means1, color='black')
pylab.plot(means2, color='blue')
pylab.show()
# Y = [exp(known[0]*x[0] + known[1]*x[1]) for x in X]
# Y1 = [int(poisson(exp(known[0]*x[0] + known[1]*x[1]),1)[0]) for x in X]
Y1 = [int(poisson(known[0]*x[0] + known[1]*x[1],1)[0]) for x in X]
# Y1 = [int(exp(known[0]*x[0] + known[1]*x[1])) for x in X]
initial = median(Y1)
if initial > 0:
initial = log(initial)
initial = [initial, 0]
st = time()
result = robust_glm(
flex.double(X),
flex.double(Y1),
flex.double(initial),
max_iter=1000)
assert result.converged()
tt += time() - st
beta = list(result.parameters())
print beta
Y2 = [exp(beta[0]*x[0] + beta[1]*x[1]) for x in X]
P = [1.0 for x in X]
result = glm(
flex.double(X),
seed(0)
means1 = []
means2 = []
for k in range(10):
print(k)
a = list(poisson(100, 100))
# a[4] = 10
# a[50] = 100
means1.append(sum(a) / len(a))
# means2.append(glm2(a))
X = flex.double([1] * len(a))
X.reshape(flex.grid(len(a), 1))
Y = flex.double(a)
B = flex.double([0])
result = robust_glm(X, Y, B, family="poisson")
print(list(result.parameters()))
print(list(glm2(a)))
from matplotlib import pylab
print("MOM1: ", sum(means1) / len(means1))
print("MOM2: ", sum(means2) / len(means2))
pylab.plot(means1, color="black")
pylab.plot(means2, color="blue")
pylab.show()
# Y = [exp(known[0]*x[0] + known[1]*x[1]) for x in X]
# Y1 = [int(poisson(exp(known[0]*x[0] + known[1]*x[1]),1)[0]) for x in X]
Y1 = [int(poisson(known[0] * x[0] + known[1] * x[1], 1)[0]) for x in X]
# Y1 = [int(exp(known[0]*x[0] + known[1]*x[1])) for x in X]
initial = median(Y1)
if initial > 0:
initial = log(initial)
initial = [initial, 0]
st = time()
result = robust_glm(flex.double(X),
flex.double(Y1),
flex.double(initial),
max_iter=1000)
assert result.converged()
tt += time() - st
beta = list(result.parameters())
print(beta)
Y2 = [exp(beta[0] * x[0] + beta[1] * x[1]) for x in X]
P = [1.0 for x in X]
result = glm(
flex.double(X),
flex.double(Y1),