|
Bernoulli(x,
p)
Log-Like Bernoulli >>> Bernoulli([0,1,1,1,0,0,1,1],0.5)
-5.54517744448 |
|
|
|
Beta(x,
a,
b)
Log-Like Beta >>> Beta([.2,.3,.7,.6,.4],2,5) -0.434845728904 |
|
|
|
Binomial(x,
n,
p)
Binomial Log-Likelihood >>> Binomial([2,3],6,0.3)
-2.81280615454 |
|
|
|
Categor(x,
hist)
Categorical Log-likelihood generalization of a Bernoulli process for
variables with any constant number of discrete values. |
|
|
|
Gamma(x,
alpha,
beta)
Log-Like Gamma >>> Gamma([2,3,7,6,4],2,2) -11.015748357 |
|
|
|
Lognormal(x,
mu,
tau)
Lognormal Log-likelihood mu: mean tau: precision (1/sd) >>>
Lognormal([0.5,1,1.2],0,0.5) -3.15728720569 |
|
|
|
Negbin(x,
r,
p)
Negative Binomial Log-Likelihood |
|
|
|
Normal(x,
mu,
tau)
Normal Log-like mu: mean tau: precision (1/sd) >>>
Normal([0],0,1) -0.918938533205 |
|
|
|
Poisson(x,
mu)
Poisson Log-Likelihood function >>> Poisson([2],2)
-1.30685281944 |
|
|
|
Simple(x,
w,
a,
start=0)
find out what it is. |
|
|
|
Weibull(x,
alpha,
beta)
Log-Like Weibull >>> Weibull([2,1,0.3,.5,1.7],1.5,3)
-7.811955373 |
|
|
|
ALLOW_THREADS = 1
|
|
BUFSIZE = 10000
|
|
CLIP = 0
|
|
ERR_CALL = 3
|
|
ERR_DEFAULT = 0
|
|
ERR_DEFAULT2 = 2084
|
|
ERR_IGNORE = 0
|
|
ERR_LOG = 5
|
|
ERR_PRINT = 4
|
|
ERR_RAISE = 2
|
|
ERR_WARN = 1
|
|
FLOATING_POINT_SUPPORT = 1
|
|
FPE_DIVIDEBYZERO = 1
|
|
FPE_INVALID = 8
|
|
FPE_OVERFLOW = 2
|
|
FPE_UNDERFLOW = 4
|
|
False_ = False
|
|
Inf = inf
|
|
Infinity = inf
|
|
MAXDIMS = 32
|
|
NAN = nan
|
|
NINF = -inf
|
|
NZERO = -0.0
|
|
NaN = nan
|
|
PINF = inf
|
|
PZERO = 0.0
|
|
RAISE = 2
|
|
SHIFT_DIVIDEBYZERO = 0
|
|
SHIFT_INVALID = 9
|
|
SHIFT_OVERFLOW = 3
|
|
SHIFT_UNDERFLOW = 6
|
|
ScalarType = ( <type 'int'>, <type 'float'>, <type 'complex'>, ...
|
|
True_ = True
|
|
UFUNC_BUFSIZE_DEFAULT = 10000
|
|
UFUNC_PYVALS_NAME = ' UFUNC_PYVALS '
|
|
WRAP = 1
|
|
absolute = <ufunc 'absolute'>
|
|
add = <ufunc 'add'>
|
|
arccos = <ufunc 'arccos'>
|
|
arccosh = <ufunc 'arccosh'>
|
|
arcsin = <ufunc 'arcsin'>
|
|
arcsinh = <ufunc 'arcsinh'>
|
|
arctan = <ufunc 'arctan'>
|
|
arctan2 = <ufunc 'arctan2'>
|
|
arctanh = <ufunc 'arctanh'>
|
|
bitwise_and = <ufunc 'bitwise_and'>
|
|
bitwise_not = <ufunc 'invert'>
|
|
bitwise_or = <ufunc 'bitwise_or'>
|
|
bitwise_xor = <ufunc 'bitwise_xor'>
|
|
c_ = <numpy.lib.index_tricks.CClass object at 0xa11152c>
|
|
cast = {<type 'numpy.int64'>: <function <lambda> at 0x9aef374>...
|
|
ceil = <ufunc 'ceil'>
|
|
conj = <ufunc 'conjugate'>
|
|
conjugate = <ufunc 'conjugate'>
|
|
cos = <ufunc 'cos'>
|
|
cosh = <ufunc 'cosh'>
|
|
degrees = <ufunc 'degrees'>
|
|
divide = <ufunc 'divide'>
|
|
e = 2.71828182846
|
|
equal = <ufunc 'equal'>
|
|
exp = <ufunc 'exp'>
|
|
expm1 = <ufunc 'expm1'>
|
|
fabs = <ufunc 'fabs'>
|
|
floor = <ufunc 'floor'>
|
|
floor_divide = <ufunc 'floor_divide'>
|
|
fmod = <ufunc 'fmod'>
|
|
frexp = <ufunc 'frexp'>
|
|
gammaln = <ufunc 'gammaln'>
|
|
greater = <ufunc 'greater'>
|
|
greater_equal = <ufunc 'greater_equal'>
|
|
hypot = <ufunc 'hypot'>
|
|
index_exp = <numpy.lib.index_tricks.IndexExpression object at ...
|
|
inf = inf
|
|
infty = inf
|
|
invert = <ufunc 'invert'>
|
|
isfinite = <ufunc 'isfinite'>
|
|
isinf = <ufunc 'isinf'>
|
|
isnan = <ufunc 'isnan'>
|
|
ldexp = <ufunc 'ldexp'>
|
|
left_shift = <ufunc 'left_shift'>
|
|
less = <ufunc 'less'>
|
|
less_equal = <ufunc 'less_equal'>
|
|
little_endian = True
|
|
log = <ufunc 'log'>
|
|
log10 = <ufunc 'log10'>
|
|
log1p = <ufunc 'log1p'>
|
|
logical_and = <ufunc 'logical_and'>
|
|
logical_not = <ufunc 'logical_not'>
|
|
logical_or = <ufunc 'logical_or'>
|
|
logical_xor = <ufunc 'logical_xor'>
|
|
maximum = <ufunc 'maximum'>
|
|
mgrid = <numpy.lib.index_tricks.nd_grid object at 0xa10af8c>
|
|
minimum = <ufunc 'minimum'>
|
|
mod = <ufunc 'remainder'>
|
|
modf = <ufunc 'modf'>
|
|
multiply = <ufunc 'multiply'>
|
|
nan = nan
|
|
nbytes = {<type 'numpy.int64'>: 8, <type 'numpy.int16'>: 2, <t...
|
|
negative = <ufunc 'negative'>
|
|
newaxis = None
|
|
not_equal = <ufunc 'not_equal'>
|
|
ogrid = <numpy.lib.index_tricks.nd_grid object at 0xa11102c>
|
|
ones_like = <ufunc 'ones_like'>
|
|
pi = 3.14159265359
|
|
power = <ufunc 'power'>
|
|
r_ = <numpy.lib.index_tricks.RClass object at 0xa11118c>
|
|
radians = <ufunc 'radians'>
|
|
reciprocal = <ufunc 'reciprocal'>
|
|
remainder = <ufunc 'remainder'>
|
|
right_shift = <ufunc 'right_shift'>
|
|
rint = <ufunc 'rint'>
|
|
s_ = <numpy.lib.index_tricks.IndexExpression object at 0xa111aec>
|
|
sctypeDict = { 0: <type 'numpy.bool_'>, 1: <type 'numpy.int8'>, ...
|
|
sctypeNA = { ' ? ' : ' Bool ' , ' B ' : ' UInt8 ' , ' Bool ' : <type 'numpy.bo...
|
|
sctypes = { ' complex ' : [ <type 'numpy.complex64'>, <type 'numpy....
|
|
sign = <ufunc 'sign'>
|
|
signbit = <ufunc 'signbit'>
|
|
sin = <ufunc 'sin'>
|
|
sinh = <ufunc 'sinh'>
|
|
sqrt = <ufunc 'sqrt'>
|
|
square = <ufunc 'square'>
|
|
subtract = <ufunc 'subtract'>
|
|
tan = <ufunc 'tan'>
|
|
tanh = <ufunc 'tanh'>
|
|
true_divide = <ufunc 'true_divide'>
|
|
typeDict = { 0: <type 'numpy.bool_'>, 1: <type 'numpy.int8'>, 2...
|
|
typeNA = { ' ? ' : ' Bool ' , ' B ' : ' UInt8 ' , ' Bool ' : <type 'numpy.bool...
|
|
typecodes = { ' All ' : ' ?bhilqpBHILQPfdgFDGSUVO ' , ' AllFloat ' : ' fd ...
|