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This commit is contained in:
Raúl Marín 2019-12-19 17:48:38 +01:00
parent 30ac9af1f9
commit 1985c1273b
2 changed files with 8 additions and 8 deletions
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8
src/py/crankshaft/crankshaft/regression/glm/iwls.py
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@ -49,7 +49,7 @@ def iwls(y, x, family, offset=1.0, ini_betas=None, tol=1.0e-8, max_iter=200, wi=
if isinstance(family, Binomial): if isinstance(family, Binomial):
y = family.link._clean(y) y = family.link._clean(y)
if isinstance(family, Poisson): if isinstance(family, Poisson):
y_off = y/offset y_off = y/offset
y_off = family.starting_mu(y_off) y_off = family.starting_mu(y_off)
v = family.predict(y_off) v = family.predict(y_off)
mu = family.starting_mu(y) mu = family.starting_mu(y)
@ -58,13 +58,13 @@ def iwls(y, x, family, offset=1.0, ini_betas=None, tol=1.0e-8, max_iter=200, wi=
v = family.predict(mu) v = family.predict(mu)
while diff > tol and n_iter < max_iter: while diff > tol and n_iter < max_iter:
n_iter += 1 n_iter += 1
w = family.weights(mu) w = family.weights(mu)
z = v + (family.link.deriv(mu)*(y-mu)) z = v + (family.link.deriv(mu)*(y-mu))
w = np.sqrt(w) w = np.sqrt(w)
if type(x) != np.ndarray: if type(x) != np.ndarray:
w = sp.csr_matrix(w) w = sp.csr_matrix(w)
z = sp.csr_matrix(z) z = sp.csr_matrix(z)
wx = spmultiply(x, w, array_out=False) wx = spmultiply(x, w, array_out=False)
wz = spmultiply(z, w, array_out=False) wz = spmultiply(z, w, array_out=False)
if wi is None: if wi is None:

8
src/py/crankshaft/crankshaft/regression/gwr/base/search.py
View File

@ -47,14 +47,14 @@ def golden_section(a, c, delta, function, tol, max_iter, int_score=False):
while np.abs(diff) > tol and iters < max_iter: while np.abs(diff) > tol and iters < max_iter:
iters += 1 iters += 1
if int_score: if int_score:
b = np.round(b) b = np.round(b)
d = np.round(d) d = np.round(d)
score_a = function(a) score_a = function(a)
score_b = function(b) score_b = function(b)
score_c = function(c) score_c = function(c)
score_d = function(d) score_d = function(d)
if score_b <= score_d: if score_b <= score_d:
opt_val = b opt_val = b
opt_score = score_b opt_score = score_b
@ -73,7 +73,7 @@ def golden_section(a, c, delta, function, tol, max_iter, int_score=False):
#d = np.round(b) #d = np.round(b)
#if int_score: #if int_score:
# opt_val = np.round(opt_val) # opt_val = np.round(opt_val)
output.append((opt_val, opt_score)) output.append((opt_val, opt_score))
diff = score_b - score_d diff = score_b - score_d
score = opt_score score = opt_score