pep8'ed transformations module

GPy/core/parameterized.py

@@ -195,15 +195,15 @@ class Parameterized(object):
 
     def constrain_negative(self, regexp):
         """ Set negative constraints. """
-        self.constrain(regexp, transformations.negative_logexp())
+        self.constrain(regexp, transformations.Negative_logexp())
 
     def constrain_positive(self, regexp):
         """ Set positive constraints. """
-        self.constrain(regexp, transformations.logexp())
+        self.constrain(regexp, transformations.Logexp())
 
     def constrain_bounded(self, regexp, lower, upper):
         """ Set bounded constraints. """
-        self.constrain(regexp, transformations.logistic(lower, upper))
+        self.constrain(regexp, transformations.Logistic(lower, upper))
 
     def all_constrained_indices(self):
         if len(self.constrained_indices) or len(self.fixed_indices):
@@ -212,7 +212,7 @@ class Parameterized(object):
             return np.empty(shape=(0,))
 
     def constrain(self, regexp, transform):
-        assert isinstance(transform, transformations.transformation)
+        assert isinstance(transform, transformations.Transformation)
 
         matches = self.grep_param_names(regexp)
         overlap = set(matches).intersection(set(self.all_constrained_indices()))
@@ -278,7 +278,7 @@ class Parameterized(object):
 
     def _untransform_params(self, x):
         """
-        The transformation required for _set_params_transformed.
+        The Transformation required for _set_params_transformed.
 
         This moves the vector x seen by the optimiser (unconstrained) to the
         valid parameter vector seen by the model

GPy/core/transformations.py

@@ -7,7 +7,7 @@ from GPy.core.domains import POSITIVE, NEGATIVE, BOUNDED
 import sys 
 lim_val = -np.log(sys.float_info.epsilon) 
 
-class transformation(object):
+class Transformation(object):
     domain = None
     def f(self, x):
         raise NotImplementedError
@@ -24,7 +24,7 @@ class transformation(object):
     def __str__(self):
         raise NotImplementedError
 
-class logexp(transformation):
+class Logexp(Transformation):
     domain = POSITIVE
     def f(self, x):
         return np.where(x>lim_val, x, np.log(1. + np.exp(x)))
@@ -39,22 +39,22 @@ class logexp(transformation):
     def __str__(self):
         return '(+ve)'
 
-class negative_logexp(transformation):
+class Negative_logexp(Transformation):
     domain = NEGATIVE
     def f(self, x):
-        return -logexp.f(x) #np.log(1. + np.exp(x))
+        return -Logexp.f(x)  # np.log(1. + np.exp(x))
     def finv(self, f):
-        return logexp.finv(-f) #np.log(np.exp(-f) - 1.)
+        return Logexp.finv(-f)  # np.log(np.exp(-f) - 1.)
     def gradfactor(self, f):
-        return -logexp.gradfactor(-f)
+        return -Logexp.gradfactor(-f)
         #ef = np.exp(-f)
         #return -(ef - 1.) / ef
     def initialize(self, f):
-        return -logexp.initialize(f) #np.abs(f)
+        return -Logexp.initialize(f)  # np.abs(f)
     def __str__(self):
         return '(-ve)'
 
-class logexp_clipped(logexp):
+class LogexpClipped(Logexp):
     max_bound = 1e100
     min_bound = 1e-10
     log_max_bound = np.log(max_bound)
@@ -81,8 +81,8 @@ class logexp_clipped(logexp):
     def __str__(self):
         return '(+ve_c)'
 
-class exponent(transformation):
-    # TODO: can't allow this to go to zero, need to set a lower bound. Similar with negative exponent below. See old MATLAB code.
+class Exponent(Transformation):
+    # TODO: can't allow this to go to zero, need to set a lower bound. Similar with negative Exponent below. See old MATLAB code.
     domain = POSITIVE
     def f(self, x):
         return np.where(x<lim_val, np.where(x>-lim_val, np.exp(x), np.exp(-lim_val)), np.exp(lim_val))
@@ -97,20 +97,20 @@ class exponent(transformation):
     def __str__(self):
         return '(+ve)'
 
-class negative_exponent(exponent):
+class NegativeExponent(Exponent):
     domain = NEGATIVE
     def f(self, x):
-        return -exponent.f(x)
+        return -Exponent.f(x)
     def finv(self, f):
-        return exponent.finv(-f)
+        return Exponent.finv(-f)
     def gradfactor(self, f):
         return f
     def initialize(self, f):
-        return -exponent.initialize(f) #np.abs(f)
+        return -Exponent.initialize(f) #np.abs(f)
     def __str__(self):
         return '(-ve)'
 
-class square(transformation):
+class Square(Transformation):
     domain = POSITIVE
     def f(self, x):
         return x ** 2
@@ -123,7 +123,7 @@ class square(transformation):
     def __str__(self):
         return '(+sq)'
 
-class logistic(transformation):
+class Logistic(Transformation):
     domain = BOUNDED
     def __init__(self, lower, upper):
         assert lower < upper

GPy/examples/dimensionality_reduction.py

@@ -5,7 +5,7 @@ import numpy as np
 from matplotlib import pyplot as plt, cm
 
 import GPy
-from GPy.core.transformations import logexp
+from GPy.core.transformations import Logexp
 from GPy.models.bayesian_gplvm import BayesianGPLVM
 from GPy.likelihoods.gaussian import Gaussian
 
@@ -88,7 +88,7 @@ def sparseGPLVM_oil(optimize=True, N=100, Q=6, num_inducing=15, max_iters=50):
 
 def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False):
     from GPy.util.datasets import swiss_roll_generated
-    from GPy.core.transformations import logexp_clipped
+    from GPy.core.transformations import LogexpClipped
 
     data = swiss_roll_generated(N=N, sigma=sigma)
     Y = data['Y']
@@ -155,7 +155,7 @@ def BGPLVM_oil(optimize=True, N=200, Q=7, num_inducing=40, max_iters=1000, plot=
     m = GPy.models.BayesianGPLVM(Yn, Q, kernel=kernel, num_inducing=num_inducing, **k)
     m.data_labels = data['Y'][:N].argmax(axis=1)
 
-    # m.constrain('variance|leng', logexp_clipped())
+    # m.constrain('variance|leng', LogexpClipped())
     # m['.*lengt'] = m.X.var(0).max() / m.X.var(0)
     m['noise'] = Yn.Y.var() / 100.
 
@@ -272,7 +272,7 @@ def bgplvm_simulation(optimize='scg',
                       plot=True,
                       max_iters=2e4,
                       plot_sim=False):
-#     from GPy.core.transformations import logexp_clipped
+#     from GPy.core.transformations import LogexpClipped
     D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
     slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
 
@@ -285,7 +285,7 @@ def bgplvm_simulation(optimize='scg',
     k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
     m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
 
-    # m.constrain('variance|noise', logexp_clipped())
+    # m.constrain('variance|noise', LogexpClipped())
     m['noise'] = Y.var() / 100.
 
     if optimize:
@@ -340,7 +340,7 @@ def brendan_faces():
     # m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=100)
 
     # optimize
-    m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
+    m.constrain('rbf|noise|white', GPy.core.transformations.LogexpClipped())
 
     m.optimize('scg', messages=1, max_f_eval=10000)