pmlearn/gaussian_process/gpr.py
Killed 38 out of 55 mutantsSurvived
Survived mutation testing. These mutants show holes in your test suite.Mutant 3
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -36,7 +36,7 @@
if self.trace is None:
raise NotFittedError('Run fit on the model before predict.')
- num_samples = X.shape[0]
+ num_samples = X.shape[1]
if self.cached_model is None:
self.cached_model = self.create_model()
Mutant 10
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -48,7 +48,7 @@
f_pred = self.gp.conditional("f_pred", X)
self.ppc = pm.sample_ppc(self.trace,
vars=[f_pred],
- samples=2000)
+ samples=2001)
if return_std:
return self.ppc['f_pred'].mean(axis=0), \
Mutant 18
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -322,7 +322,7 @@
"""
def __init__(self, prior_mean=None, kernel=None):
- self.ppc = None
+ self.ppc = ""
self.gp = None
self.num_training_samples = None
self.num_pred = None
Mutant 19
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -323,7 +323,7 @@
def __init__(self, prior_mean=None, kernel=None):
self.ppc = None
- self.gp = None
+ self.gp = ""
self.num_training_samples = None
self.num_pred = None
self.prior_mean = prior_mean
Mutant 21
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -325,7 +325,7 @@
self.ppc = None
self.gp = None
self.num_training_samples = None
- self.num_pred = None
+ self.num_pred = ""
self.prior_mean = prior_mean
self.kernel = kernel
Mutant 22
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -326,7 +326,7 @@
self.gp = None
self.num_training_samples = None
self.num_pred = None
- self.prior_mean = prior_mean
+ self.prior_mean = None
self.kernel = kernel
super(SparseGaussianProcessRegressor, self).__init__()
Mutant 23
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -327,7 +327,7 @@
self.num_training_samples = None
self.num_pred = None
self.prior_mean = prior_mean
- self.kernel = kernel
+ self.kernel = None
super(SparseGaussianProcessRegressor, self).__init__()
Mutant 28
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -352,7 +352,7 @@
'model_output': model_output,
}
- self.gp = None
+ self.gp = ""
model = pm.Model()
with model:
Mutant 30
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -356,7 +356,7 @@
model = pm.Model()
with model:
- length_scale = pm.Gamma('length_scale', alpha=2, beta=1,
+ length_scale = pm.Gamma('XXlength_scaleXX', alpha=2, beta=1,
shape=(1, self.num_pred))
signal_variance = pm.HalfCauchy('signal_variance', beta=5,
shape=1)
Mutant 32
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -356,7 +356,7 @@
model = pm.Model()
with model:
- length_scale = pm.Gamma('length_scale', alpha=2, beta=1,
+ length_scale = pm.Gamma('length_scale', alpha=2, beta=2,
shape=(1, self.num_pred))
signal_variance = pm.HalfCauchy('signal_variance', beta=5,
shape=1)
Mutant 35
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -358,7 +358,7 @@
with model:
length_scale = pm.Gamma('length_scale', alpha=2, beta=1,
shape=(1, self.num_pred))
- signal_variance = pm.HalfCauchy('signal_variance', beta=5,
+ signal_variance = pm.HalfCauchy('XXsignal_varianceXX', beta=5,
shape=1)
noise_variance = pm.HalfCauchy('noise_variance', beta=5,
shape=1)
Mutant 36
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -358,7 +358,7 @@
with model:
length_scale = pm.Gamma('length_scale', alpha=2, beta=1,
shape=(1, self.num_pred))
- signal_variance = pm.HalfCauchy('signal_variance', beta=5,
+ signal_variance = pm.HalfCauchy('signal_variance', beta=6,
shape=1)
noise_variance = pm.HalfCauchy('noise_variance', beta=5,
shape=1)
Mutant 39
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -360,7 +360,7 @@
shape=(1, self.num_pred))
signal_variance = pm.HalfCauchy('signal_variance', beta=5,
shape=1)
- noise_variance = pm.HalfCauchy('noise_variance', beta=5,
+ noise_variance = pm.HalfCauchy('XXnoise_varianceXX', beta=5,
shape=1)
if self.kernel is None:
Mutant 40
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -360,7 +360,7 @@
shape=(1, self.num_pred))
signal_variance = pm.HalfCauchy('signal_variance', beta=5,
shape=1)
- noise_variance = pm.HalfCauchy('noise_variance', beta=5,
+ noise_variance = pm.HalfCauchy('noise_variance', beta=6,
shape=1)
if self.kernel is None:
Mutant 45
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -364,7 +364,7 @@
shape=1)
if self.kernel is None:
- cov_function = signal_variance ** 2 * RBF(
+ cov_function = signal_variance ** 3 * RBF(
input_dim=self.num_pred,
ls=length_scale)
else:
Mutant 52
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -381,7 +381,7 @@
# initialize 20 inducing points with K-means
# gp.util
- Xu = pm.gp.util.kmeans_inducing_points(20,
+ Xu = pm.gp.util.kmeans_inducing_points(21,
X=model_input.get_value())
y = self.gp.marginal_likelihood('y',
Mutant 54
--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -384,7 +384,7 @@
Xu = pm.gp.util.kmeans_inducing_points(20,
X=model_input.get_value())
- y = self.gp.marginal_likelihood('y',
+ y = self.gp.marginal_likelihood('XXyXX',
X=model_input.get_value(),
Xu=Xu,
y=model_output.get_value(),