def tst_glm():
from scitbx.glmtbx import 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])
P = flex.double([1 for i in range(n_obs)])
result = glm(X, Y, B, P, family="poisson", max_iter=100)
assert (abs(c - exp(result.parameters()[0])) < 0.1 * c)
print('OK')
def tst_glm():
from scitbx.glmtbx import 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])
P = flex.double([1 for i in range(n_obs)])
result = glm(X, Y, B, P, family="poisson", max_iter=100)
assert(abs(c - exp(result.parameters()[0])) < 0.1*c)
print 'OK'
nobs = 100
X1 = [1] * nobs
X2 = [i for i in range(nobs)]
# X1 = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
# X2 = [0, 0, 1, 2, 4, 5, 4, 3, 2, 0, 0]
X = flex.double(item for a, b in zip(X1, X2) for item in [a, b])
X.reshape(flex.grid(nobs, 2))
# Y = flex.double([poisson(a*X1[i]+c*X2[i],1)[0] for i in range(nobs)])
Y = flex.double(
[poisson(c * X1[i] + a * X2[i], 1)[0] for i in range(nobs)])
B = flex.double([0, 0])
P = flex.double([1] * nobs)
B1 = glm33(X, Y, B, P)
print(list(B1))
result = glm(X, Y, B, P, max_iter=1000)
print(list(result.parameters()))
exit(0)
# for k in range(10):
# X = flex.double([item for i in range(100) for item in [1,i-50]])
# X.reshape(flex.grid(100, 2))
# Y = flex.double([poisson(a*i+c,1)[0] for i in range(100)])
# B = flex.double([0, 0])
# P = flex.double([1] * len(Y))
# B = glm33(X,Y,B,P)
# print exp(B[0]), exp(B[1])
Y2 = []
Y3 = []
c = 10 nobs = 100 X1 = [1] * nobs X2 = [i for i in range(nobs)] # X1 = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] # X2 = [0, 0, 1, 2, 4, 5, 4, 3, 2, 0, 0] X = flex.double(item for a, b in zip(X1, X2) for item in [a, b]) X.reshape(flex.grid(nobs, 2)) # Y = flex.double([poisson(a*X1[i]+c*X2[i],1)[0] for i in range(nobs)]) Y = flex.double([poisson(c*X1[i]+a*X2[i],1)[0] for i in range(nobs)]) B = flex.double([0, 0]) P = flex.double([1] * nobs) B1 = glm33(X,Y,B,P) print list(B1) result = glm(X,Y,B,P, max_iter=1000) print list(result.parameters()) exit(0) # for k in range(10): # X = flex.double([item for i in range(100) for item in [1,i-50]]) # X.reshape(flex.grid(100, 2)) # Y = flex.double([poisson(a*i+c,1)[0] for i in range(100)]) # B = flex.double([0, 0]) # P = flex.double([1] * len(Y)) # B = glm33(X,Y,B,P) # print exp(B[0]), exp(B[1]) Y2 = [] Y3 = []
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),
flex.double(initial),
flex.double(P),
max_iter=1000)
assert result.converged()
beta = list(result.parameters())
print beta
Y3 = [exp(beta[0]*x[0] + beta[1]*x[1]) for x in X]
m1 = max(Y1)
m2 = max(Y2)
m3 = max(Y3)
m = ceil(max([m1,m2,m3]))
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),
flex.double(initial),
flex.double(P),
max_iter=1000,
)
assert result.converged()
beta = list(result.parameters())
print(beta)
Y3 = [exp(beta[0] * x[0] + beta[1] * x[1]) for x in X]
m1 = max(Y1)
m2 = max(Y2)
m3 = max(Y3)
m = ceil(max([m1, m2, m3]))
