pmlearn/gaussian_process/gpr.py

Survived

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 5

--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -38,7 +38,7 @@
 
         num_samples = X.shape[0]
 
-        if self.cached_model is None:
+        if self.cached_model is not None:
             self.cached_model = self.create_model()
 
         self._set_shared_vars({'model_input': X,

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 20

--- pmlearn/gaussian_process/gpr.py
+++ pmlearn/gaussian_process/gpr.py
@@ -324,7 +324,7 @@
     def __init__(self, prior_mean=None, kernel=None):
         self.ppc = None
         self.gp = None
-        self.num_training_samples = None
+        self.num_training_samples = ""
         self.num_pred = None
         self.prior_mean = prior_mean
         self.kernel = kernel

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 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 31

--- 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=3, 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 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(),