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Attempt to align numbers to right

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This commit is contained in:
Neil Lawrence 2014-10-16 04:29:06 +01:00
parent 5189ccaf38
commit 082ae150de
6 changed files with 2 additions and 504 deletions
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30
GPy/kern/_src/eq.py
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@ -1,30 +0,0 @@
try:
import sympy as sym
sympy_available=True
except ImportError:
sympy_available=False
import numpy as np
from symbolic import Symbolic
class Eq(Symbolic):
"""
The exponentiated quadratic covariance as a symbolic function.
"""
def __init__(self, input_dim, output_dim=1, variance=1.0, lengthscale=1.0, name='Eq'):
parameters = {'variance' : variance, 'lengthscale' : lengthscale}
x = sym.symbols('x_:' + str(input_dim))
z = sym.symbols('z_:' + str(input_dim))
variance = sym.var('variance',positive=True)
lengthscale = sym.var('lengthscale', positive=True)
dist_string = ' + '.join(['(x_%i - z_%i)**2' %(i, i) for i in range(input_dim)])
from sympy.parsing.sympy_parser import parse_expr
dist = parse_expr(dist_string)
# this is the covariance function
f = variance*sym.exp(-dist/(2*lengthscale**2))
# extra input dim is to signify the output dimension.
super(Eq, self).__init__(input_dim=input_dim, k=f, output_dim=output_dim, parameters=parameters, name=name)

30
GPy/kern/_src/heat_eqinit.py
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@ -1,30 +0,0 @@
try:
import sympy as sym
sympy_available=True
except ImportError:
sympy_available=False
import numpy as np
from symbolic import Symbolic
class Heat_eqinit(Symbolic):
"""
A symbolic covariance based on laying down an initial condition of the heat equation with an exponentiated quadratic covariance. The covariance then has multiple outputs which are interpreted as observations of the diffused process with different diffusion coefficients (or at different times).
"""
def __init__(self, input_dim, output_dim=1, param=None, name='Heat_eqinit'):
x = sym.symbols('x_:' + str(input_dim))
z = sym.symbols('z_:' + str(input_dim))
scale = sym.var('scale_i scale_j',positive=True)
lengthscale = sym.var('lengthscale_i lengthscale_j', positive=True)
shared_lengthscale = sym.var('shared_lengthscale', positive=True)
dist_string = ' + '.join(['(x_%i - z_%i)**2' %(i, i) for i in range(input_dim)])
from sympy.parsing.sympy_parser import parse_expr
dist = parse_expr(dist_string)
# this is the covariance function
f = scale_i*scale_j*sym.exp(-dist/(2*(shared_lengthscale**2 + lengthscale_i*lengthscale_j)))
# extra input dim is to signify the output dimension.
super(Heat_eqinit, self).__init__(input_dim=input_dim+1, k=f, output_dim=output_dim, name=name)