From e055b5429ac1cd9f07d86988205991a0245ca34c Mon Sep 17 00:00:00 2001 From: Taco de Wolff Date: Thu, 10 Dec 2020 15:26:09 +0100 Subject: [PATCH] Release 0.2.2 --- docs/bnse.html | 42 ++--- docs/data.html | 146 ++++++++++------ docs/dataset.html | 70 ++++---- docs/errors.html | 10 +- docs/{kernels => gpr}/config.html | 110 ++++++++---- docs/gpr/distribution.html | 185 ++++++++++++++++++++ docs/{kernels => gpr}/index.html | 43 ++--- docs/{kernels => gpr}/kernel.html | 141 +++++++-------- docs/{kernels => gpr}/mean.html | 14 +- docs/{kernels => gpr}/model.html | 148 ++++++++-------- docs/{kernels => gpr}/multioutput.html | 88 +++++----- docs/{kernels => gpr}/parameter.html | 100 +++++------ docs/{kernels => gpr}/singleoutput.html | 217 ++++++++++++++++++------ docs/{kernels => gpr}/util.html | 8 +- docs/index.html | 47 ++--- docs/model.html | 71 ++++---- docs/{ => models}/conv.html | 82 ++++----- docs/{ => models}/csm.html | 82 ++++----- docs/models/index.html | 87 ++++++++++ docs/{ => models}/mosm.html | 102 +++++------ docs/{ => models}/sm.html | 90 +++++----- docs/{ => models}/sm_lmc.html | 82 ++++----- docs/plot.html | 4 +- docs/serie.html | 62 +++---- setup.py | 4 +- 25 files changed, 1248 insertions(+), 787 deletions(-) rename docs/{kernels => gpr}/config.html (60%) create mode 100644 docs/gpr/distribution.html rename docs/{kernels => gpr}/index.html (74%) rename docs/{kernels => gpr}/kernel.html (77%) rename docs/{kernels => gpr}/mean.html (91%) rename docs/{kernels => gpr}/model.html (83%) rename docs/{kernels => gpr}/multioutput.html (83%) rename docs/{kernels => gpr}/parameter.html (79%) rename docs/{kernels => gpr}/singleoutput.html (67%) rename docs/{kernels => gpr}/util.html (94%) rename docs/{ => models}/conv.html (86%) rename docs/{ => models}/csm.html (87%) create mode 100644 docs/models/index.html rename docs/{ => models}/mosm.html (89%) rename docs/{ => models}/sm.html (87%) rename docs/{ => models}/sm_lmc.html (87%) diff --git a/docs/bnse.html b/docs/bnse.html index f0805123..a6c2903f 100644 --- a/docs/bnse.html +++ b/docs/bnse.html @@ -25,7 +25,7 @@

Module mogptk.bnse

Expand source code -Browse git +Browse git 
import numpy as np
 import matplotlib.pyplot as plt
@@ -271,7 +271,7 @@ 
Functions

 

 
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def Spec_Mix(x,y, gamma, theta, sigma=1):
     return sigma**2 * np.exp(-gamma*outersum(x,-y)**2)*np.cos(2*np.pi*theta*outersum(x,-y))

@@ -285,7 +285,7 @@ 
Functions

 

 
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def Spec_Mix_sine(x,y, gamma, theta, sigma=1):
     return sigma**2 * np.exp(-gamma*outersum(x,-y)**2)*np.sin(2*np.pi*theta*outersum(x,-y))

@@ -299,7 +299,7 @@ 
Functions

 

 
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def Spec_Mix_spectral(x, y, alpha, gamma, theta, sigma=1):
     magnitude = np.pi * sigma**2 / (np.sqrt(alpha*(alpha + 2*gamma)))
@@ -314,7 +314,7 @@ 
Functions

 

 
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def freq_covariances(x, y, alpha, gamma, theta, sigma=1, kernel = 'sm'):
     if kernel == 'sm':
@@ -335,7 +335,7 @@ 
Functions

 

 
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def outersum(a,b):
     # equivalent to np.outer(a,np.ones_like(b))+np.outer(np.ones_like(a),b) when a and b are arrays
@@ -351,7 +351,7 @@ 
Functions

 

 
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def time_freq_SM_im(x, y, alpha, gamma, theta, sigma=1):
     at = alpha/(np.pi**2)
@@ -368,7 +368,7 @@ 
Functions

 

 
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def time_freq_SM_re(x, y, alpha, gamma, theta, sigma=1):
     at = alpha/(np.pi**2)
@@ -385,7 +385,7 @@ 
Functions

 

 
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def time_freq_covariances(x, t, alpha, gamma, theta, sigma, kernel = 'sm'):
     if kernel == 'sm':
@@ -408,7 +408,7 @@ 
Classes

 

 
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class bse:
     def __init__(self, space_input, space_output):
@@ -599,7 +599,7 @@ 
Methods

 

 
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def compute_moments(self):
     #posterior moments for time
@@ -628,7 +628,7 @@ 
Methods

 

 
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def dnlogp(self, hypers):
     sigma = np.exp(hypers[0])
@@ -662,7 +662,7 @@ 
Methods

 

 
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def get_freq_peaks(self):
     x = self.w
@@ -691,7 +691,7 @@ 
Methods

 

 
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def neg_log_likelihood(self):
     Y = self.y
@@ -709,7 +709,7 @@ 
Methods

 

 
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def nlogp(self, hypers):
     sigma = np.exp(hypers[0])
@@ -732,7 +732,7 @@ 
Methods

 

 
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def plot_freq_posterior(self):
     #posterior moments for frequency
@@ -767,7 +767,7 @@ 
Methods

 

 
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def plot_power_spectral_density(self, how_many, flag=None):
     #posterior moments for frequency
@@ -800,7 +800,7 @@ 
Methods

 

 
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def plot_time_posterior(self, flag=None):
     #posterior moments for time
@@ -828,7 +828,7 @@ 
Methods

 

 
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def set_freqspace(self, max_freq, dimension=500):
     self.w = np.linspace(0, max_freq, dimension)

@@ -842,7 +842,7 @@ 
Methods

 

 
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def set_labels(self, time_label, signal_label):
     self.time_label = time_label
@@ -857,7 +857,7 @@ 
Methods

 

 
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def train(self):
     hypers0 = np.array([np.log(self.sigma), np.log(self.gamma), np.log(self.theta), np.log(self.sigma_n)])
diff --git a/docs/data.html b/docs/data.html
index c0e18379..bcb3cfeb 100644
--- a/docs/data.html
+++ b/docs/data.html
@@ -25,7 +25,7 @@ 
Module mogptk.data

 

 
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import re
 import copy
@@ -548,6 +548,14 @@ 
Module mogptk.data

         return X, np.array(self.Y[~self.mask])
 
     ################################################################
+
+    def reset(self):
+        """
+        Reset the data set and undo the removal of data points. That is, this reverts any calls to `remove_randomly`, `remove_range`, `remove_relative_range`, `remove_random_ranges`, and `remove_index`.
+        """
+        self.mask[:] = True
+        for i in range(len(self.removed_ranges)):
+            self.removed_ranges[i] = []
     
     def remove_randomly(self, n=None, pct=None):
         """
@@ -1004,7 +1012,8 @@ 
Module mogptk.data

             transformed (boolean): Display transformed Y data as used for training.
 
         Returns:
-            matplotlib.axes.Axes
+            matplotlib.figure.Figure: only if `ax` is not set
+            matplotlib.axes.Axes: only if `ax` is not set
 
         Examples:
             >>> ax = data.plot()
@@ -1015,8 +1024,9 @@ 
Module mogptk.data

         if self.get_input_dims() == 2:
             raise NotImplementedError("two dimensional input data not yet implemented") # TODO
 
+        fig = None
         if ax is None:
-            _, ax = plt.subplots(1, 1, figsize=(12, 3.0), squeeze=True, constrained_layout=True)
+            fig, ax = plt.subplots(1, 1, figsize=(12, 3.0), squeeze=True, constrained_layout=True)
 
         legends = []
         colors = list(matplotlib.colors.TABLEAU_COLORS)
@@ -1092,8 +1102,10 @@ 
Module mogptk.data

 
         if legend:
             legend_rows = (len(legends)-1)/5 + 1
-            ax.legend(handles=legends, loc="upper center", bbox_to_anchor=(0.5,(3.0+0.7+0.3*legend_rows)/3.0), ncol=5)
-        return ax
+            ax.legend(handles=legends, loc="upper center", bbox_to_anchor=(0.5,(3.0+0.5+0.3*legend_rows)/3.0), ncol=5)
+
+        if fig is not None:
+            return fig, ax
 
     def plot_spectrum(self, title=None, method='ls', ax=None, per=None, maxfreq=None, transformed=False):
         """
@@ -1363,7 +1375,7 @@ 
Examples

 

 
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def LoadFunction(f, start, end, n, var=0.0, name="", random=False):
     """
@@ -1489,7 +1501,7 @@ 
Examples

 

 
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class Data:
     def __init__(self, X, Y, name=None, x_labels=None, y_label=None):
@@ -1896,6 +1908,14 @@ 
Examples

         return X, np.array(self.Y[~self.mask])
 
     ################################################################
+
+    def reset(self):
+        """
+        Reset the data set and undo the removal of data points. That is, this reverts any calls to `remove_randomly`, `remove_range`, `remove_relative_range`, `remove_random_ranges`, and `remove_index`.
+        """
+        self.mask[:] = True
+        for i in range(len(self.removed_ranges)):
+            self.removed_ranges[i] = []
     
     def remove_randomly(self, n=None, pct=None):
         """
@@ -2352,7 +2372,8 @@ 
Examples

             transformed (boolean): Display transformed Y data as used for training.
 
         Returns:
-            matplotlib.axes.Axes
+            matplotlib.figure.Figure: only if `ax` is not set
+            matplotlib.axes.Axes: only if `ax` is not set
 
         Examples:
             >>> ax = data.plot()
@@ -2363,8 +2384,9 @@ 
Examples

         if self.get_input_dims() == 2:
             raise NotImplementedError("two dimensional input data not yet implemented") # TODO
 
+        fig = None
         if ax is None:
-            _, ax = plt.subplots(1, 1, figsize=(12, 3.0), squeeze=True, constrained_layout=True)
+            fig, ax = plt.subplots(1, 1, figsize=(12, 3.0), squeeze=True, constrained_layout=True)
 
         legends = []
         colors = list(matplotlib.colors.TABLEAU_COLORS)
@@ -2440,8 +2462,10 @@ 
Examples

 
         if legend:
             legend_rows = (len(legends)-1)/5 + 1
-            ax.legend(handles=legends, loc="upper center", bbox_to_anchor=(0.5,(3.0+0.7+0.3*legend_rows)/3.0), ncol=5)
-        return ax
+            ax.legend(handles=legends, loc="upper center", bbox_to_anchor=(0.5,(3.0+0.5+0.3*legend_rows)/3.0), ncol=5)
+
+        if fig is not None:
+            return fig, ax
 
     def plot_spectrum(self, title=None, method='ls', ax=None, per=None, maxfreq=None, transformed=False):
         """
@@ -2574,7 +2598,7 @@ 
Examples

 

 
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def aggregate(self, duration, f=np.mean):
     """
@@ -2617,7 +2641,7 @@ 
Examples

 

 
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def clear_predictions(self):
     """
@@ -2643,7 +2667,7 @@ 
Examples

 

 
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def copy(self):
     """
@@ -2678,7 +2702,7 @@ 
Examples

 

 
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def filter(self, start, end):
     """
@@ -2728,7 +2752,7 @@ 
Examples

 

 
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def get_bnse_estimation(self, Q=1, n=1000):
     """
@@ -2798,7 +2822,7 @@ 
Examples

 

 
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def get_data(self, transformed=False):
     """
@@ -2839,7 +2863,7 @@ 
Examples

 

 
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def get_input_dims(self):
     """
@@ -2877,7 +2901,7 @@ 
Examples

 

 
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def get_lombscargle_estimation(self, Q=1, n=10000):
     """
@@ -2950,7 +2974,7 @@ 
Examples

 

 
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def get_name(self):
     """
@@ -2979,7 +3003,7 @@ 
Examples

 

 
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def get_nyquist_estimation(self):
     """
@@ -3027,7 +3051,7 @@ 
Examples

 

 
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def get_prediction(self, name, sigma=2.0, transformed=False):
     """
@@ -3081,7 +3105,7 @@ 
Examples

 

 
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def get_prediction_names(self):
     """
@@ -3110,7 +3134,7 @@ 
Examples

 

 
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def get_prediction_x(self):
     """
@@ -3155,7 +3179,7 @@ 
Examples

 

 
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def get_sm_estimation(self, Q=1, method='BNSE', optimizer='Adam', iters=100, params={}, plot=False):
     """
@@ -3224,7 +3248,7 @@ 
Examples

 

 
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def get_test_data(self, transformed=False):
     """
@@ -3272,7 +3296,7 @@ 
Examples

 

 
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def get_train_data(self, transformed=False):
     """
@@ -3313,7 +3337,7 @@ 
Examples

 

 
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def has_test_data(self):
     """
@@ -3348,17 +3372,15 @@ 
Args

 
Display transformed Y data as used for training.

 
 
Returns

-

-
matplotlib.axes.Axes

-
 

-

+
matplotlib.figure.Figure: only if ax is not set
+matplotlib.axes.Axes: only if ax is not set

 
Examples

 
>>> ax = data.plot()
 

 

 
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def plot(self, pred=None, title=None, ax=None, legend=True, transformed=False):
     """
@@ -3372,7 +3394,8 @@ 
Examples

         transformed (boolean): Display transformed Y data as used for training.
 
     Returns:
-        matplotlib.axes.Axes
+        matplotlib.figure.Figure: only if `ax` is not set
+        matplotlib.axes.Axes: only if `ax` is not set
 
     Examples:
         >>> ax = data.plot()
@@ -3383,8 +3406,9 @@ 
Examples

     if self.get_input_dims() == 2:
         raise NotImplementedError("two dimensional input data not yet implemented") # TODO
 
+    fig = None
     if ax is None:
-        _, ax = plt.subplots(1, 1, figsize=(12, 3.0), squeeze=True, constrained_layout=True)
+        fig, ax = plt.subplots(1, 1, figsize=(12, 3.0), squeeze=True, constrained_layout=True)
 
     legends = []
     colors = list(matplotlib.colors.TABLEAU_COLORS)
@@ -3460,8 +3484,10 @@ 
Examples

 
     if legend:
         legend_rows = (len(legends)-1)/5 + 1
-        ax.legend(handles=legends, loc="upper center", bbox_to_anchor=(0.5,(3.0+0.7+0.3*legend_rows)/3.0), ncol=5)
-    return ax

+        ax.legend(handles=legends, loc="upper center", bbox_to_anchor=(0.5,(3.0+0.5+0.3*legend_rows)/3.0), ncol=5)
+
+    if fig is not None:
+        return fig, ax

 

 
 

@@ -3495,7 +3521,7 @@ 
Examples

 

 
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def plot_spectrum(self, title=None, method='ls', ax=None, per=None, maxfreq=None, transformed=False):
     """
@@ -3593,7 +3619,7 @@ 
Args

 

 
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def remove_index(self, index):
     """
@@ -3630,7 +3656,7 @@ 
Examples

 

 
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def remove_random_ranges(self, n, duration):
     """
@@ -3686,7 +3712,7 @@ 
Examples

 

 
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def remove_randomly(self, n=None, pct=None):
     """
@@ -3733,7 +3759,7 @@ 
Examples

 

 
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def remove_range(self, start=None, end=None):
     """
@@ -3781,7 +3807,7 @@ 
Args

 

 
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def remove_relative_range(self, start=0.0, end=1.0):
     """
@@ -3824,7 +3850,7 @@ 
Examples

 

 
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def rescale_x(self, upper=1000.0):
     """
@@ -3846,6 +3872,25 @@ 
Examples

         self.X[i].apply(t)

 

 
+

+def reset(self)
+

+

+
Reset the data set and undo the removal of data points. That is, this reverts any calls to remove_randomly, remove_range, remove_relative_range, remove_random_ranges, and remove_index.

+

+
+Expand source code
+Browse git
+
+
def reset(self):
+    """
+    Reset the data set and undo the removal of data points. That is, this reverts any calls to `remove_randomly`, `remove_range`, `remove_relative_range`, `remove_random_ranges`, and `remove_index`.
+    """
+    self.mask[:] = True
+    for i in range(len(self.removed_ranges)):
+        self.removed_ranges[i] = []

+

+

 

 def set_function(self, f)
 

@@ -3863,7 +3908,7 @@ 
Examples

 

 
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def set_function(self, f):
     """
@@ -3899,7 +3944,7 @@ 
Examples

 

 
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def set_labels(self, x_labels, y_label):
     """
@@ -3941,7 +3986,7 @@ 
Examples

 

 
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def set_name(self, name):
     """
@@ -3983,7 +4028,7 @@ 
Examples

 

 
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def set_prediction_range(self, start=None, end=None, n=None, step=None):
     """
@@ -4056,7 +4101,7 @@ 
Examples

 

 
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def set_prediction_x(self, X):
     """
@@ -4107,7 +4152,7 @@ 
Examples

 

 
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def transform(self, transformer):
     """
@@ -4185,6 +4230,7 @@ 
Dataremove_range
 
  • remove_relative_range
    • 
 
  • rescale_x
    • 
+
  • reset
    • 
 
  • set_function
    • 
 
  • set_labels
    • 
 
  • set_name
    • 
diff --git a/docs/dataset.html b/docs/dataset.html
index 9c6a5052..a6d5c35d 100644
--- a/docs/dataset.html
+++ b/docs/dataset.html
@@ -25,7 +25,7 @@ 
    Module mogptk.dataset
    
 
    
 
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    import copy
 
@@ -678,8 +678,8 @@ 
    Module mogptk.dataset
    
 
         legends = {}
         for channel in range(self.get_output_dims()):
-            ax = self.channels[channel].plot(pred=pred, ax=axes[channel,0], transformed=transformed)
-            legend = ax.get_legend()
+            self.channels[channel].plot(pred=pred, ax=axes[channel,0], transformed=transformed)
+            legend = axes[channel,0].get_legend()
             for text, handle in zip(legend.texts, legend.legendHandles):
                 if text.get_text() == "Training Points":
                     handle = plt.Line2D([0], [0], ls='-', color='k', marker='.', ms=10, label='Training Points')
@@ -770,7 +770,7 @@ 
    Examples
    
 
    
 
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    def LoadCSV(filename, x_col=0, y_col=1, name=None, **kwargs):
     """
@@ -827,7 +827,7 @@ 
    Examples
    
 
    
 
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    def LoadDataFrame(df, x_col=0, y_col=1, name=None):
     """
@@ -940,7 +940,7 @@ 
    Examples
    
 
    
 
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    class DataSet:
     """
@@ -1490,8 +1490,8 @@ 
    Examples
    
 
         legends = {}
         for channel in range(self.get_output_dims()):
-            ax = self.channels[channel].plot(pred=pred, ax=axes[channel,0], transformed=transformed)
-            legend = ax.get_legend()
+            self.channels[channel].plot(pred=pred, ax=axes[channel,0], transformed=transformed)
+            legend = axes[channel,0].get_legend()
             for text, handle in zip(legend.texts, legend.legendHandles):
                 if text.get_text() == "Training Points":
                     handle = plt.Line2D([0], [0], ls='-', color='k', marker='.', ms=10, label='Training Points')
@@ -1565,7 +1565,7 @@ 
    Examples
    
 
    
 
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    def aggregate(self, duration, f=np.mean):
     """
@@ -1602,7 +1602,7 @@ 
    Examples
    
 
    
 
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    def append(self, arg):
     """
@@ -1639,7 +1639,7 @@ 
    Examples
    
 
    
 
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    def clear_predictions(self):
     """
@@ -1665,7 +1665,7 @@ 
    Examples
    
 
    
 
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    def copy(self):
     """
@@ -1700,7 +1700,7 @@ 
    Examples
    
 
    
 
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    def filter(self, start, end):
     """
@@ -1737,7 +1737,7 @@ 
    Examples
    
 
    
 
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    def get(self, index):
     """
@@ -1789,7 +1789,7 @@ 
    Examples
    
 
    
 
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    def get_bnse_estimation(self, Q=1, n=1000):
     """
@@ -1841,7 +1841,7 @@ 
    Examples
    
 
    
 
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    def get_data(self, transformed=False):
     """
@@ -1881,7 +1881,7 @@ 
    Examples
    
 
    
 
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    def get_index(self, index):
     """
@@ -1923,7 +1923,7 @@ 
    Examples
    
 
    
 
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    def get_input_dims(self):
     """
@@ -1966,7 +1966,7 @@ 
    Examples
    
 
    
 
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    def get_lombscargle_estimation(self, Q=1, n=10000):
     """
@@ -2012,7 +2012,7 @@ 
    Examples
    
 
    
 
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    def get_names(self):
     """
@@ -2044,7 +2044,7 @@ 
    Examples
    
 
    
 
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    def get_nyquist_estimation(self):
     """
@@ -2079,7 +2079,7 @@ 
    Examples
    
 
    
 
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    def get_output_dims(self):
     """
@@ -2126,7 +2126,7 @@ 
    Examples
    
 
    
 
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-Browse git
+Browse git
 
 
    def get_prediction(self, name, sigma=2.0, transformed=False):
     """
@@ -2175,7 +2175,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def get_prediction_x(self):
     """
@@ -2228,7 +2228,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def get_sm_estimation(self, Q=1, method='BNSE', optimizer='Adam', iters=100, params={}, plot=False):
     """
@@ -2284,7 +2284,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def get_test_data(self, transformed=False):
     """
@@ -2326,7 +2326,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def get_train_data(self, transformed=False):
     """
@@ -2372,7 +2372,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def plot(self, pred=None, title=None, figsize=None, legend=True, transformed=False):
     """
@@ -2400,8 +2400,8 @@ 
    Examples
    
 
     legends = {}
     for channel in range(self.get_output_dims()):
-        ax = self.channels[channel].plot(pred=pred, ax=axes[channel,0], transformed=transformed)
-        legend = ax.get_legend()
+        self.channels[channel].plot(pred=pred, ax=axes[channel,0], transformed=transformed)
+        legend = axes[channel,0].get_legend()
         for text, handle in zip(legend.texts, legend.legendHandles):
             if text.get_text() == "Training Points":
                 handle = plt.Line2D([0], [0], ls='-', color='k', marker='.', ms=10, label='Training Points')
@@ -2445,7 +2445,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def plot_spectrum(self, title=None, method='ls', per=None, maxfreq=None, figsize=None, transformed=False):
     """
@@ -2501,7 +2501,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def rescale_x(self, upper=1000.0):
     """
@@ -2540,7 +2540,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def set_prediction_range(self, start, end, n=None, step=None):
     """
@@ -2601,7 +2601,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def set_prediction_x(self, x):
     """
@@ -2648,7 +2648,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def transform(self, transformer):
     """
diff --git a/docs/errors.html b/docs/errors.html
index e884c60a..6adee94f 100644
--- a/docs/errors.html
+++ b/docs/errors.html
@@ -25,7 +25,7 @@ 
    Module mogptk.errors
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    import numpy as np
 import pandas as pd
@@ -173,7 +173,7 @@ 
    Example
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def error(*models, X=None, Y=None, per_channel=False, transformed=False, disp=False):
     """
@@ -266,7 +266,7 @@ 
    Example
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def mean_absolute_error(y_true, y_pred):
     """
@@ -284,7 +284,7 @@ 
    Example
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def mean_absolute_percentage_error(y_true, y_pred):
     """
@@ -304,7 +304,7 @@ 
    Example
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def root_mean_squared_error(y_true, y_pred):
     """
diff --git a/docs/kernels/config.html b/docs/gpr/config.html
similarity index 60%
rename from docs/kernels/config.html
rename to docs/gpr/config.html
index c91d4f3d..1714b1a9 100644
--- a/docs/kernels/config.html
+++ b/docs/gpr/config.html
@@ -4,7 +4,7 @@
 
 
 
-mogptk.kernels.config API documentation
+mogptk.gpr.config API documentation
 
 
 
@@ -19,13 +19,13 @@
 
    
 
    
 
    
-
    Module mogptk.kernels.config
    
+
    Module mogptk.gpr.config
    
 
    
 
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    import torch
 
@@ -39,18 +39,30 @@ 
    Module mogptk.kernels.config
    
 config = Config()
 
 def use_single_precision():
+    """
+    Use single precision (float32) for all tensors. This may be much faster on GPUs, but has reduced precision and may more often cause numerical instability.
+    """
     config.dtype = torch.float32
 
 def use_double_precision():
+    """
+    Use double precision (float64) for all tensors. This is the recommended precision for numerical stability, but can be significantly slower.
+    """
     config.dtype = torch.float64
 
 def use_cpu(n=None):
+    """
+    Use the CPU instead of the GPU for tensor calculations. This is the default if no GPU is available. If you have more than one CPU, you can use a specific CPU by setting `n`.
+    """
     if n is None:
         config.device = torch.device('cpu')
     else:
         config.device = torch.device('cpu', n)
 
 def use_gpu(n=None):
+    """
+    Use the GPU instead of the CPU for tensor calculations. This is the default if a GPU is available. If you have more than one GPU, you can use a specific GPU by setting `n`.
+    """
     if not torch.cuda.is_available():
         logger.error("CUDA is not available")
     elif n is not None and (not isinstance(n, int) or n < 0 or torch.cuda.device_count() <= n):
@@ -61,6 +73,9 @@ 
    Module mogptk.kernels.config
    
         config.device = torch.device('cuda', n)
 
 def print_gpu_information():
+    """
+    Print information about whether CUDA is supported, and if so which GPU is being used.
+    """
     if not torch.cuda.is_available():
         print("CUDA is not available")
         return
@@ -73,6 +88,9 @@ 
    Module mogptk.kernels.config
    
         print("%2d  %s%s" % (n, torch.cuda.get_device_name(n), " (selected)" if n == current else ""))
 
 def set_positive_minimum(val):
+    """
+    Set the positive minimum for kernel parameters. This is usually slightly larger than zero to avoid numerical instabilities. Default is at 1e-8.
+    """
     config.positive_minimum = val
    
 
    
 
    
@@ -83,17 +101,20 @@ 
    Module mogptk.kernels.config
    
 
    
 
    Functions
    
 
    
-
    
+
    
 def print_gpu_information()
 
    
 
    
-
    
+
    Print information about whether CUDA is supported, and if so which GPU is being used.
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def print_gpu_information():
+    """
+    Print information about whether CUDA is supported, and if so which GPU is being used.
+    """
     if not torch.cuda.is_available():
         print("CUDA is not available")
         return
@@ -106,62 +127,74 @@ 
    Functions
    
         print("%2d  %s%s" % (n, torch.cuda.get_device_name(n), " (selected)" if n == current else ""))
    
 
    
 
    
-
    
+
    
 def set_positive_minimum(val)
 
    
 
    
-
    
+
    Set the positive minimum for kernel parameters. This is usually slightly larger than zero to avoid numerical instabilities. Default is at 1e-8.
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def set_positive_minimum(val):
+    """
+    Set the positive minimum for kernel parameters. This is usually slightly larger than zero to avoid numerical instabilities. Default is at 1e-8.
+    """
     config.positive_minimum = val
    
 
    
 
    
-
    
+
    
 def use_cpu(n=None)
 
    
 
    
-
    
+
    Use the CPU instead of the GPU for tensor calculations. This is the default if no GPU is available. If you have more than one CPU, you can use a specific CPU by setting n.
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def use_cpu(n=None):
+    """
+    Use the CPU instead of the GPU for tensor calculations. This is the default if no GPU is available. If you have more than one CPU, you can use a specific CPU by setting `n`.
+    """
     if n is None:
         config.device = torch.device('cpu')
     else:
         config.device = torch.device('cpu', n)
    
 
    
 
    
-
    
+
    
 def use_double_precision()
 
    
 
    
-
    
+
    Use double precision (float64) for all tensors. This is the recommended precision for numerical stability, but can be significantly slower.
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def use_double_precision():
+    """
+    Use double precision (float64) for all tensors. This is the recommended precision for numerical stability, but can be significantly slower.
+    """
     config.dtype = torch.float64
    
 
    
 
    
-
    
+
    
 def use_gpu(n=None)
 
    
 
    
-
    
+
    Use the GPU instead of the CPU for tensor calculations. This is the default if a GPU is available. If you have more than one GPU, you can use a specific GPU by setting n.
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def use_gpu(n=None):
+    """
+    Use the GPU instead of the CPU for tensor calculations. This is the default if a GPU is available. If you have more than one GPU, you can use a specific GPU by setting `n`.
+    """
     if not torch.cuda.is_available():
         logger.error("CUDA is not available")
     elif n is not None and (not isinstance(n, int) or n < 0 or torch.cuda.device_count() <= n):
@@ -172,17 +205,20 @@ 
    Functions
    
         config.device = torch.device('cuda', n)
    
 
    
 
    
-
    
+
    
 def use_single_precision()
 
    
 
    
-
    
+
    Use single precision (float32) for all tensors. This may be much faster on GPUs, but has reduced precision and may more often cause numerical instability.
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def use_single_precision():
+    """
+    Use single precision (float32) for all tensors. This may be much faster on GPUs, but has reduced precision and may more often cause numerical instability.
+    """
     config.dtype = torch.float32
    
 
    
 
    
@@ -191,7 +227,7 @@ 
    Functions
    
 
    
 
    Classes
    
 
    
-
    
+
    
 class Config
 
    
 
    
@@ -199,7 +235,7 @@ 
    Classes
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class Config:
     dtype = torch.float64
@@ -211,15 +247,15 @@ 
    Classes
    
 
    
 
    Class variables
    
 
    
-
    var device
    
+
    var device
    
 
    
 
    
 
    
-
    var dtype
    
+
    var dtype
    
 
    
 
    
 
    
-
    var positive_minimum
    
+
    var positive_minimum
    
 
    
 
    Convert a string or number to a floating point number, if possible.
    
 
    
@@ -236,27 +272,27 @@ 
    Index
    
 
      
 
    • Super-module
      
 
 
      • 
 
    • Functions
      
 
 
      • 
 
    • Classes
      
 
diff --git a/docs/gpr/distribution.html b/docs/gpr/distribution.html
new file mode 100644
index 00000000..3be9a90f
--- /dev/null
+++ b/docs/gpr/distribution.html
@@ -0,0 +1,185 @@
+
+
+
+
+
+
+mogptk.gpr.distribution API documentation
+
+
+
+
+
+
+
+
+
+
+
+
      
+
      
+
      
+
      Module mogptk.gpr.distribution
      
+
      
+
      
+
      
+
+Expand source code
+Browse git
+
+
      import torch
+from . import config
+
+class Distribution:
+    def log_p(self, x):
+        raise NotImplementedError()
+
+class Gaussian(Distribution):
+    def __init__(self, mu=torch.tensor(0.0), sigma=torch.tensor(1.0)):
+        if mu.shape != sigma.shape:
+            raise ValueError("mu and sigma need to have the same shape")
+
+        self.mu = mu.to(config.device, config.dtype)
+        self.sigma = sigma.to(config.device, config.dtype)
+        self.log_p_constant = -0.5*torch.log(2.0*np.pi*sigma)
+
+    def log_p(self, x):
+        if x.shape != self.mu.shape:
+            raise ValueError("x must match shape of mu and sigma")
+        return self.log_p_constant - 0.5*(x-self.mu).T.mm(self.sigma.mm(x-self.mu))
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Classes
      
+
      
+
      
+class Distribution
+
      
+
      
+
      
+
      
+
+Expand source code
+Browse git
+
+
      class Distribution:
+    def log_p(self, x):
+        raise NotImplementedError()
      
+
      
+
      Subclasses
      
+
+
      Methods
      
+
      
+
      
+def log_p(self, x)
+
      
+
      
+
      
+
      
+
+Expand source code
+Browse git
+
+
      def log_p(self, x):
+    raise NotImplementedError()
      
+
      
+
      
+
      
+
      
+
      
+class Gaussian
+(mu=tensor(0.), sigma=tensor(1.))
+
      
+
      
+
      
+
      
+
+Expand source code
+Browse git
+
+
      class Gaussian(Distribution):
+    def __init__(self, mu=torch.tensor(0.0), sigma=torch.tensor(1.0)):
+        if mu.shape != sigma.shape:
+            raise ValueError("mu and sigma need to have the same shape")
+
+        self.mu = mu.to(config.device, config.dtype)
+        self.sigma = sigma.to(config.device, config.dtype)
+        self.log_p_constant = -0.5*torch.log(2.0*np.pi*sigma)
+
+    def log_p(self, x):
+        if x.shape != self.mu.shape:
+            raise ValueError("x must match shape of mu and sigma")
+        return self.log_p_constant - 0.5*(x-self.mu).T.mm(self.sigma.mm(x-self.mu))
      
+
      
+
      Ancestors
      
+
+
      Methods
      
+
      
+
      
+def log_p(self, x)
+
      
+
      
+
      
+
      
+
+Expand source code
+Browse git
+
+
      def log_p(self, x):
+    if x.shape != self.mu.shape:
+        raise ValueError("x must match shape of mu and sigma")
+    return self.log_p_constant - 0.5*(x-self.mu).T.mm(self.sigma.mm(x-self.mu))
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
+
      
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/kernels/index.html b/docs/gpr/index.html
similarity index 74%
rename from docs/kernels/index.html
rename to docs/gpr/index.html
index 188ddd52..e0d6d1d6 100644
--- a/docs/kernels/index.html
+++ b/docs/gpr/index.html
@@ -4,7 +4,7 @@
 
 
 
-mogptk.kernels API documentation
+mogptk.gpr API documentation
 
 
 
@@ -19,13 +19,13 @@
 
      
 
      
 
      
-
      Module mogptk.kernels
      
+
      Module mogptk.gpr
      
 
      
 
      
 
      
 
 Expand source code
-Browse git
+Browse git
 
 
      from .config import *
 from .parameter import *
@@ -39,35 +39,39 @@ 
      Module mogptk.kernels
      
 
      
 
      Sub-modules
      
 
      
-
      mogptk.kernels.config
      
+
      mogptk.gpr.config
      
 
      
 
      
 
      
-
      mogptk.kernels.kernel
      
+
      mogptk.gpr.distribution
      
 
      
 
      
 
      
-
      mogptk.kernels.mean
      
+
      mogptk.gpr.kernel
      
 
      
 
      
 
      
-
      mogptk.kernels.model
      
+
      mogptk.gpr.mean
      
 
      
 
      
 
      
-
      mogptk.kernels.multioutput
      
+
      mogptk.gpr.model
      
 
      
 
      
 
      
-
      mogptk.kernels.parameter
      
+
      mogptk.gpr.multioutput
      
 
      
 
      
 
      
-
      mogptk.kernels.singleoutput
      
+
      mogptk.gpr.parameter
      
 
      
 
      
 
      
-
      mogptk.kernels.util
      
+
      mogptk.gpr.singleoutput
      
+
      
+
      
+
      
+
      mogptk.gpr.util
      
 
      
 
      
 
      
@@ -93,14 +97,15 @@ 
      Index
      
 
      • 
 
    • Sub-modules
      
 
 
      • 
 
    
diff --git a/docs/kernels/kernel.html b/docs/gpr/kernel.html
similarity index 77%
rename from docs/kernels/kernel.html
rename to docs/gpr/kernel.html
index 25346d9e..4139c33e 100644
--- a/docs/kernels/kernel.html
+++ b/docs/gpr/kernel.html
@@ -4,7 +4,7 @@
 
 
 
-mogptk.kernels.kernel API documentation
+mogptk.gpr.kernel API documentation
 
 
 
@@ -19,13 +19,13 @@
 
    
 
    
 
    
-
    Module mogptk.kernels.kernel
    
+
    Module mogptk.gpr.kernel
    
 
    
 
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    import torch
 import copy
@@ -229,7 +229,7 @@ 
    Module mogptk.kernels.kernel
    
 
    
 
    Classes
    
 
    
-
    
+
    
 class AddKernel
 (*kernels, name='Add')
 
    
@@ -238,7 +238,7 @@ 
    Classes
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class AddKernel(Kernel):
     def __init__(self, *kernels, name="Add"):
@@ -253,15 +253,15 @@ 
    Classes
    
 
    
 
    Ancestors
    
 
 
    Subclasses
    
 
 
    Methods
    
 
    
-
    
+
    
 def K(self, X1, X2=None)
 
    
 
    
@@ -269,7 +269,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def K(self, X1, X2=None):
     return torch.stack([kernel(X1,X2) for kernel in self.kernels], dim=2).sum(dim=2)
    
@@ -277,7 +277,7 @@ 
    Methods
    
 
    
 
    
 
    
-
    
+
    
 class AutomaticRelevanceDeterminationKernel
 (kernel, input_dims, name='ARD')
 
    
@@ -286,7 +286,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class AutomaticRelevanceDeterminationKernel(MulKernel):
     def __init__(self, kernel, input_dims, name="ARD"):
@@ -297,11 +297,11 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
-
    
+
    
 class Kernel
 (input_dims=None, active_dims=None, name=None)
 
    
@@ -310,7 +310,7 @@ 
    Ancestors
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class Kernel:
     def __init__(self, input_dims=None, active_dims=None, name=None):
@@ -412,26 +412,27 @@ 
    Ancestors
    
 
    
 
    Subclasses
    
 
 
    Instance variables
    
 
    
-
    var active_dims
    
+
    var active_dims
    
 
    
 
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    @property
 def active_dims(self):
@@ -441,7 +442,7 @@ 
    Instance variables
    
 
    
 
    Methods
    
 
    
-
    
+
    
 def K(self, X1, X2=None)
 
    
 
    
@@ -449,13 +450,13 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def K(self, X1, X2=None):
     raise NotImplementedError()
    
 
    
 
    
-
    
+
    
 def distance(self, X1, X2=None)
 
    
 
    
@@ -463,7 +464,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def distance(self, X1, X2=None):
     # X1 is NxD, X2 is MxD, then ret is NxMxD
@@ -472,7 +473,7 @@ 
    Methods
    
     return X1.unsqueeze(1) - X2
    
 
    
 
    
-
    
+
    
 def squared_distance(self, X1, X2=None)
 
    
 
    
@@ -480,7 +481,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def squared_distance(self, X1, X2=None):
     # X1 is NxD, X2 is MxD, then ret is NxMxD
@@ -492,7 +493,7 @@ 
    Methods
    
 
    
 
    
 
-
    
+
    
 class MixtureKernel
 (kernel, Q, name='Mixture')
 
    
@@ -501,7 +502,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class MixtureKernel(AddKernel):
     def __init__(self, kernel, Q, name="Mixture"):
@@ -510,11 +511,11 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
-
    
+
    
 class MulKernel
 (*kernels, name='Mul')
 
    
@@ -523,7 +524,7 @@ 
    Ancestors
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class MulKernel(Kernel):
     def __init__(self, *kernels, name="Mul"):
@@ -538,15 +539,15 @@ 
    Ancestors
    
 
    
 
    Ancestors
    
 
 
    Subclasses
    
 
 
    Methods
    
 
    
-
    
+
    
 def K(self, X1, X2=None)
 
    
 
    
@@ -554,7 +555,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def K(self, X1, X2=None):
     return torch.stack([kernel(X1,X2) for kernel in self.kernels], dim=2).prod(dim=2)
    
@@ -562,7 +563,7 @@ 
    Methods
    
 
    
 
    
 
-
    
+
    
 class MultiOutputKernel
 (output_dims, input_dims=None, active_dims=None, name=None)
 
    
@@ -571,7 +572,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class MultiOutputKernel(Kernel):
     # The MultiOutputKernel is a base class for multi output kernels. It assumes that the first dimension of X contains channel IDs (integers) and calculate the final kernel matrix accordingly. Concretely, it will call the Ksub method for derived kernels from this class, which should return the kernel matrix between channel i and j, given inputs X1 and X2. This class will automatically split and recombine the input vectors and kernel matrices respectively, in order to create the final kernel matrix of the multi output kernel.
@@ -631,19 +632,19 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Subclasses
    
 
 
    Methods
    
 
    
-
    
+
    
 def K(self, X1, X2=None)
 
    
 
    
@@ -651,7 +652,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def K(self, X1, X2=None):
     # X has shape (data_points,1+input_dims) where the first column is the channel ID
@@ -695,7 +696,7 @@ 
    Methods
    
     return res
    
 
    
 
    
-
    
+
    
 def Ksub(self, i, j, X1, X2=None)
 
    
 
    
@@ -703,7 +704,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def Ksub(self, i, j, X1, X2=None):
     raise NotImplementedError()
    
@@ -722,43 +723,43 @@ 
    Index
    
 
      
 
    • Super-module
      
 
 
      • 
 
    • Classes
      
 
diff --git a/docs/kernels/mean.html b/docs/gpr/mean.html
similarity index 91%
rename from docs/kernels/mean.html
rename to docs/gpr/mean.html
index 57477fa5..dfb702b1 100644
--- a/docs/kernels/mean.html
+++ b/docs/gpr/mean.html
@@ -4,7 +4,7 @@
 
 
 
-mogptk.kernels.mean API documentation
+mogptk.gpr.mean API documentation
 
 
 
@@ -19,13 +19,13 @@
 
      
 
      
 
      
-
      Module mogptk.kernels.mean
      
+
      Module mogptk.gpr.mean
      
 
      
 
      
 
      
 
 Expand source code
-Browse git
+Browse git
 
 
      from . import Parameter
 
@@ -62,7 +62,7 @@ 
      Module mogptk.kernels.mean
      
 
      
 
      Classes
      
 
      
-
      
+
      
 class Mean
 (name=None)
 
      
@@ -71,7 +71,7 @@ 
      Classes
      
 
      
 
 Expand source code
-Browse git
+Browse git
 
 
      class Mean:
     def __init__(self, name=None):
@@ -108,13 +108,13 @@ 
      Index
      
 
        
 
      • Super-module
        
 
 
        • 
 
      • Classes
        
 
 
        • 
diff --git a/docs/kernels/model.html b/docs/gpr/model.html
similarity index 83%
rename from docs/kernels/model.html
rename to docs/gpr/model.html
index 99101788..b63a41cc 100644
--- a/docs/kernels/model.html
+++ b/docs/gpr/model.html
@@ -4,7 +4,7 @@
 
 
 
-mogptk.kernels.model API documentation
+mogptk.gpr.model API documentation
 
 
 
@@ -19,13 +19,13 @@
 
        
 
        
 
        
-
        Module mogptk.kernels.model
        
+
        Module mogptk.gpr.model
        
 
        
 
        
 
        
 
 Expand source code
-Browse git
+Browse git
 
 
        import torch
 import numpy as np
@@ -38,11 +38,11 @@ 
        Module mogptk.kernels.model
        
 class Model:
     def __init__(self, kernel, X, y, mean=None, name=None):
         if not issubclass(type(kernel), Kernel):
-            raise ValueError("kernel must derive from mogptk.kernels.Kernel")
+            raise ValueError("kernel must derive from mogptk.gpr.Kernel")
         X, y = self._check_input(X, y)
         if mean is not None:
             if not issubclass(type(mean), Mean):
-                raise ValueError("mean must derive from mogptk.kernels.Mean")
+                raise ValueError("mean must derive from mogptk.gpr.Mean")
             mu = mean(X).reshape(-1,1)
             if mu.shape != y.shape:
                 raise ValueError("mean and y data must match shapes: %s != %s" % (mu.shape, y.shape))
@@ -152,7 +152,7 @@ 
        Module mogptk.kernels.model
        
         except Exception as e:
             vals = [["Name", "Range", "Value"]]
             for name, p in zip(self._param_names, self._params):
-                vals.append([name, param_range(p.lower, p.upper, p.trainable), str(p.constrained.detach().numy())])
+                vals.append([name, param_range(p.lower, p.upper, p.trainable), str(p.constrained.detach().numpy())])
 
             nameWidth = max([len(val[0]) for val in vals])
             rangeWidth = max([len(val[1]) for val in vals])
@@ -197,11 +197,11 @@ 
        Module mogptk.kernels.model
        
             if n is None:
                 S = 1
 
-            mu, var = self.predict(Z, full_var=True)  # MxD and MxMxD
+            mu, var = self.predict(Z, full=True, numpy=False)  # MxD and MxMxD
             u = torch.normal(torch.zeros(Z.shape[0], S, device=config.device, dtype=config.dtype), torch.tensor(1.0, device=config.device, dtype=config.dtype))  # MxS
             L = torch.cholesky(var + 1e-6*torch.ones(Z.shape[0]).diagflat())  # MxM
             samples = mu + L.mm(u)  # MxS
-            if num is None:
+            if n is None:
                 samples = samples.squeeze()
             return samples.detach().numpy()
 
@@ -228,7 +228,7 @@ 
        Module mogptk.kernels.model
        
         p -= self.log_marginal_likelihood_constant
         return p#/self.X.shape[0]  # dividing by the number of data points normalizes the learning rate
 
-    def predict(self, Z):
+    def predict(self, Z, full=False, numpy=True):
         with torch.no_grad():
             Z = self._check_input(Z)  # MxD
 
@@ -247,8 +247,12 @@ 
        Module mogptk.kernels.model
        
                 mu = Ks.T.mm(torch.cholesky_solve(self.y,L))  # Mx1
 
             var = Kss - v.T.mm(v)  # MxM
-            var = var.diag().reshape(-1,1)  # Mx1
-            return mu.detach().numpy(), var.detach().numpy()
        
+            if not full:
+                var = var.diag().reshape(-1,1)  # Mx1
+            if numpy:
+                return mu.detach().numpy(), var.detach().numpy()
+            else:
+                return mu.detach(), var.detach()
      
 
      
 
      
 
      
@@ -260,7 +264,7 @@ 
      Module mogptk.kernels.model
      
 
      
 
      Classes
      
 
      
-
      
+
      
 class CholeskyException
 (...)
 
      
@@ -269,7 +273,7 @@ 
      Classes
      
 
      
 
 Expand source code
-Browse git
+Browse git
 
 
      class CholeskyException(Exception):
     pass
      
@@ -280,7 +284,7 @@ 
      Ancestors
      
 
    • builtins.BaseException
      • 
 
    
 
    
-
    
+
    
 class GPR
 (kernel, X, y, noise=1.0, mean=None, name='GPR')
 
    
@@ -289,7 +293,7 @@ 
    Ancestors
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class GPR(Model):
     def __init__(self, kernel, X, y, noise=1.0, mean=None, name="GPR"):
@@ -314,7 +318,7 @@ 
    Ancestors
    
         p -= self.log_marginal_likelihood_constant
         return p#/self.X.shape[0]  # dividing by the number of data points normalizes the learning rate
 
-    def predict(self, Z):
+    def predict(self, Z, full=False, numpy=True):
         with torch.no_grad():
             Z = self._check_input(Z)  # MxD
 
@@ -333,16 +337,20 @@ 
    Ancestors
    
                 mu = Ks.T.mm(torch.cholesky_solve(self.y,L))  # Mx1
 
             var = Kss - v.T.mm(v)  # MxM
-            var = var.diag().reshape(-1,1)  # Mx1
-            return mu.detach().numpy(), var.detach().numpy()
    
+            if not full:
+                var = var.diag().reshape(-1,1)  # Mx1
+            if numpy:
+                return mu.detach().numpy(), var.detach().numpy()
+            else:
+                return mu.detach(), var.detach()
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def log_marginal_likelihood(self)
 
    
 
    
@@ -350,7 +358,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def log_marginal_likelihood(self):
     K = self.kernel(self.X) + self.noise()*torch.eye(self.X.shape[0], device=config.device, dtype=config.dtype)  # NxN
@@ -367,17 +375,17 @@ 
    Methods
    
     return p#/self.X.shape[0]  # dividing by the number of data points normalizes the learning rate
    
 
    
 
    
-
    
-def predict(self, Z)
+
    
+def predict(self, Z, full=False, numpy=True)
 
    
 
    
 
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
-
    def predict(self, Z):
+
    def predict(self, Z, full=False, numpy=True):
     with torch.no_grad():
         Z = self._check_input(Z)  # MxD
 
@@ -396,13 +404,17 @@ 
    Methods
    
             mu = Ks.T.mm(torch.cholesky_solve(self.y,L))  # Mx1
 
         var = Kss - v.T.mm(v)  # MxM
-        var = var.diag().reshape(-1,1)  # Mx1
-        return mu.detach().numpy(), var.detach().numpy()
    
+        if not full:
+            var = var.diag().reshape(-1,1)  # Mx1
+        if numpy:
+            return mu.detach().numpy(), var.detach().numpy()
+        else:
+            return mu.detach(), var.detach()
    
 
    
 
    
 
    
 
-
    
+
    
 class Model
 (kernel, X, y, mean=None, name=None)
 
    
@@ -411,16 +423,16 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class Model:
     def __init__(self, kernel, X, y, mean=None, name=None):
         if not issubclass(type(kernel), Kernel):
-            raise ValueError("kernel must derive from mogptk.kernels.Kernel")
+            raise ValueError("kernel must derive from mogptk.gpr.Kernel")
         X, y = self._check_input(X, y)
         if mean is not None:
             if not issubclass(type(mean), Mean):
-                raise ValueError("mean must derive from mogptk.kernels.Mean")
+                raise ValueError("mean must derive from mogptk.gpr.Mean")
             mu = mean(X).reshape(-1,1)
             if mu.shape != y.shape:
                 raise ValueError("mean and y data must match shapes: %s != %s" % (mu.shape, y.shape))
@@ -530,7 +542,7 @@ 
    Methods
    
         except Exception as e:
             vals = [["Name", "Range", "Value"]]
             for name, p in zip(self._param_names, self._params):
-                vals.append([name, param_range(p.lower, p.upper, p.trainable), str(p.constrained.detach().numy())])
+                vals.append([name, param_range(p.lower, p.upper, p.trainable), str(p.constrained.detach().numpy())])
 
             nameWidth = max([len(val[0]) for val in vals])
             rangeWidth = max([len(val[1]) for val in vals])
@@ -575,21 +587,21 @@ 
    Methods
    
             if n is None:
                 S = 1
 
-            mu, var = self.predict(Z, full_var=True)  # MxD and MxMxD
+            mu, var = self.predict(Z, full=True, numpy=False)  # MxD and MxMxD
             u = torch.normal(torch.zeros(Z.shape[0], S, device=config.device, dtype=config.dtype), torch.tensor(1.0, device=config.device, dtype=config.dtype))  # MxS
             L = torch.cholesky(var + 1e-6*torch.ones(Z.shape[0]).diagflat())  # MxM
             samples = mu + L.mm(u)  # MxS
-            if num is None:
+            if n is None:
                 samples = samples.squeeze()
             return samples.detach().numpy()
    
 
    
 
    Subclasses
    
 
 
    Methods
    
 
    
-
    
+
    
 def K(self, Z)
 
    
 
    
@@ -597,7 +609,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def K(self, Z):
     with torch.no_grad():
@@ -605,7 +617,7 @@ 
    Methods
    
         return self.kernel(Z).detach().numpy()
    
 
    
 
    
-
    
+
    
 def log_marginal_likelihood(self)
 
    
 
    
@@ -613,13 +625,13 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def log_marginal_likelihood(self):
     raise NotImplementedError()
    
 
    
 
    
-
    
+
    
 def log_prior(self)
 
    
 
    
@@ -627,13 +639,13 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def log_prior(self):
     return sum([p.log_prior() for p in self._params])
    
 
    
 
    
-
    
+
    
 def loss(self)
 
    
 
    
@@ -641,7 +653,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def loss(self):
     self.zero_grad()
@@ -650,7 +662,7 @@ 
    Methods
    
     return loss
    
 
    
 
    
-
    
+
    
 def parameters(self)
 
    
 
    
@@ -658,7 +670,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def parameters(self):
     for p in self._params:
@@ -666,7 +678,7 @@ 
    Methods
    
             yield p.unconstrained
    
 
    
 
    
-
    
+
    
 def print_parameters(self)
 
    
 
    
@@ -674,7 +686,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def print_parameters(self):
     def param_range(lower, upper, trainable=True):
@@ -698,7 +710,7 @@ 
    Methods
    
     except Exception as e:
         vals = [["Name", "Range", "Value"]]
         for name, p in zip(self._param_names, self._params):
-            vals.append([name, param_range(p.lower, p.upper, p.trainable), str(p.constrained.detach().numy())])
+            vals.append([name, param_range(p.lower, p.upper, p.trainable), str(p.constrained.detach().numpy())])
 
         nameWidth = max([len(val[0]) for val in vals])
         rangeWidth = max([len(val[1]) for val in vals])
@@ -706,7 +718,7 @@ 
    Methods
    
             print("%-*s  %-*s  %s" % (nameWidth, val[0], rangeWidth, val[1], val[2]))
    
 
    
 
    
-
    
+
    
 def sample(self, Z, n=None)
 
    
 
    
@@ -714,7 +726,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def sample(self, Z, n=None):
     with torch.no_grad():
@@ -722,16 +734,16 @@ 
    Methods
    
         if n is None:
             S = 1
 
-        mu, var = self.predict(Z, full_var=True)  # MxD and MxMxD
+        mu, var = self.predict(Z, full=True, numpy=False)  # MxD and MxMxD
         u = torch.normal(torch.zeros(Z.shape[0], S, device=config.device, dtype=config.dtype), torch.tensor(1.0, device=config.device, dtype=config.dtype))  # MxS
         L = torch.cholesky(var + 1e-6*torch.ones(Z.shape[0]).diagflat())  # MxM
         samples = mu + L.mm(u)  # MxS
-        if num is None:
+        if n is None:
             samples = samples.squeeze()
         return samples.detach().numpy()
    
 
    
 
    
-
    
+
    
 def zero_grad(self)
 
    
 
    
@@ -739,7 +751,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def zero_grad(self):
     for p in self._params:
@@ -765,32 +777,32 @@ 
    Index
    
 
    
-
    
+
    
 class GaussianConvolutionProcessKernel
 (output_dims, input_dims, active_dims=None, name='CONV')
 
    
@@ -296,7 +296,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class GaussianConvolutionProcessKernel(MultiOutputKernel):
     def __init__(self, output_dims, input_dims, active_dims=None, name="CONV"):
@@ -330,12 +330,12 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def Ksub(self, i, j, X1, X2=None)
 
    
 
    
@@ -343,7 +343,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def Ksub(self, i, j, X1, X2=None):
     # X has shape (data_points,input_dims)
@@ -365,7 +365,7 @@ 
    Methods
    
 
    
 
    
 
-
    
+
    
 class IndependentMultiOutputKernel
 (*kernels, output_dims=None, name='IMO')
 
    
@@ -374,7 +374,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class IndependentMultiOutputKernel(MultiOutputKernel):
     def __init__(self, *kernels, output_dims=None, name="IMO"):
@@ -397,12 +397,12 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def Ksub(self, i, j, X1, X2=None)
 
    
 
    
@@ -410,7 +410,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def Ksub(self, i, j, X1, X2=None):
     # X has shape (data_points,input_dims)
@@ -424,7 +424,7 @@ 
    Methods
    
 
    
 
    
 
-
    
+
    
 class LinearModelOfCoregionalizationKernel
 (*kernels, output_dims, input_dims, Q=None, Rq=1, name='LMC')
 
    
@@ -433,7 +433,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class LinearModelOfCoregionalizationKernel(MultiOutputKernel):
     def __init__(self, *kernels, output_dims, input_dims, Q=None, Rq=1, name="LMC"):
@@ -458,12 +458,12 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def Ksub(self, i, j, X1, X2=None)
 
    
 
    
@@ -471,7 +471,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def Ksub(self, i, j, X1, X2=None):
     # X has shape (data_points,input_dims)
@@ -482,7 +482,7 @@ 
    Methods
    
 
    
 
    
 
-
    
+
    
 class MultiOutputSpectralKernel
 (output_dims, input_dims, active_dims=None, name='MOSM')
 
    
@@ -491,7 +491,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class MultiOutputSpectralKernel(MultiOutputKernel):
     def __init__(self, output_dims, input_dims, active_dims=None, name="MOSM"):
@@ -542,12 +542,12 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def Ksub(self, i, j, X1, X2=None)
 
    
 
    
@@ -555,7 +555,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def Ksub(self, i, j, X1, X2=None):
     # X has shape (data_points,input_dims)
@@ -596,39 +596,39 @@ 
    Index
    
 
    
-
    
+
    
 class Sigmoid
 (lower=0.0, upper=1.0)
 
    
@@ -411,7 +411,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class Sigmoid(Transform):
     def __init__(self, lower=0.0, upper=1.0):
@@ -429,11 +429,11 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def forward(self, x)
 
    
 
    
@@ -441,13 +441,13 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def forward(self, x):
     return self.lower + (self.upper-self.lower)*torch.sigmoid(x)
    
 
    
 
    
-
    
+
    
 def inverse(self, y)
 
    
 
    
@@ -455,7 +455,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def inverse(self, y):
     if torch.any(y < self.lower) or torch.any(self.upper < y):
@@ -466,7 +466,7 @@ 
    Methods
    
 
    
 
    
 
-
    
+
    
 class Softplus
 (lower=0.0, beta=0.1, threshold=20.0)
 
    
@@ -475,7 +475,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class Softplus(Transform):
     def __init__(self, lower=0.0, beta=0.1, threshold=20.0):
@@ -493,11 +493,11 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def forward(self, x)
 
    
 
    
@@ -505,13 +505,13 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def forward(self, x):
     return self.lower + functional.softplus(x, beta=self.beta, threshold=self.threshold)
    
 
    
 
    
-
    
+
    
 def inverse(self, y)
 
    
 
    
@@ -519,7 +519,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def inverse(self, y):
     if torch.any(y < self.lower):
@@ -529,7 +529,7 @@ 
    Methods
    
 
    
 
    
 
-
    
+
    
 class Transform
 
    
 
    
@@ -537,7 +537,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class Transform:
     def forward(self, x):
@@ -550,12 +550,12 @@ 
    Methods
    
 
    
 
    Subclasses
    
 
 
    Methods
    
 
    
-
    
+
    
 def forward(self, x)
 
    
 
    
@@ -563,14 +563,14 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def forward(self, x):
     # unconstrained to constrained space
     raise NotImplementedError()
    
 
    
 
    
-
    
+
    
 def inverse(self, y)
 
    
 
    
@@ -578,7 +578,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def inverse(self, y):
     # constrained to unconstrained space
@@ -598,39 +598,39 @@ 
    Index
    
 
      
 
    • Super-module
      
 
 
      • 
 
    • Classes
      
 
diff --git a/docs/kernels/singleoutput.html b/docs/gpr/singleoutput.html
similarity index 67%
rename from docs/kernels/singleoutput.html
rename to docs/gpr/singleoutput.html
index 54ac944f..4c576ad5 100644
--- a/docs/kernels/singleoutput.html
+++ b/docs/gpr/singleoutput.html
@@ -4,7 +4,7 @@
 
 
 
-mogptk.kernels.singleoutput API documentation
+mogptk.gpr.singleoutput API documentation
 
 
 
@@ -19,13 +19,13 @@
 
      
 
      
 
      
-
      Module mogptk.kernels.singleoutput
      
+
      Module mogptk.gpr.singleoutput
      
 
      
 
      
 
      
 
 Expand source code
-Browse git
+Browse git
 
 
      import torch
 import numpy as np
@@ -160,7 +160,36 @@ 
      Module mogptk.kernels.singleoutput
      
         tau = self.distance(X1,X2)  # NxMxD
         exp = torch.exp(-2.0*np.pi**2 * tau**2 * self.variance().reshape(1,1,-1))  # NxMxD
         cos = torch.cos(2.0*np.pi * tau * self.mean().reshape(1,1,-1))  # NxMxD
-        return self.weight() * torch.prod(exp * cos, dim=2)
      
+        return self.weight() * torch.prod(exp * cos, dim=2)
+
+class MaternKernel(Kernel):
+    def __init__(self, nu=0.5, input_dims=None, active_dims=None, name="Matérn"):
+        super(MaternKernel, self).__init__(input_dims, active_dims, name)
+
+        if nu not in [0.5, 1.5, 2.5]:
+            raise ValueError("nu parameter must be 0.5, 1.5, or 2.5")
+
+        l = torch.rand(input_dims)
+        sigma = torch.rand(1)
+
+        self.nu = nu
+        self.l = Parameter(l, lower=1e-6)
+        self.sigma = Parameter(sigma, lower=1e-6)
+
+    def K(self, X1, X2=None):
+        # X has shape (data_points,input_dims)
+        X1,X2 = self._check_input(X1,X2)
+        if X2 is None:
+            X2 = X1
+
+        dist = torch.abs(torch.tensordot(self.distance(X1,X2), 1.0/self.l(), dims=1))
+        if self.nu == 0.5:
+            constant = 1.0
+        elif self.nu == 1.5:
+            constant = 1.0 + np.sqrt(3.0)*dist
+        elif self.nu == 2.5:
+            constant = 1.0 + np.sqrt(5.0)*dist + 5.0/3.0*dist**2
+        return self.sigma()**2 * constant * torch.exp(-np.sqrt(self.nu*2.0)*dist)
    
 
    
 
 
    
@@ -172,7 +201,7 @@ 
    Module mogptk.kernels.singleoutput
    
 
    
 
    Classes
    
 
    
-
    
+
    
 class LinearKernel
 (input_dims=None, active_dims=None, name='Linear')
 
    
@@ -181,7 +210,7 @@ 
    Classes
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class LinearKernel(Kernel):
     def __init__(self, input_dims=None, active_dims=None, name="Linear"):
@@ -201,11 +230,11 @@ 
    Classes
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def K(self, X1, X2=None)
 
    
 
    
@@ -213,7 +242,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def K(self, X1, X2=None):
     # X has shape (data_points,input_dims)
@@ -226,7 +255,81 @@ 
    Methods
    
 
    
 
    
 
    
-
    
+
    
+class MaternKernel
+(nu=0.5, input_dims=None, active_dims=None, name='Matérn')
+
    
+
    
+
    
+
    
+
+Expand source code
+Browse git
+
+
    class MaternKernel(Kernel):
+    def __init__(self, nu=0.5, input_dims=None, active_dims=None, name="Matérn"):
+        super(MaternKernel, self).__init__(input_dims, active_dims, name)
+
+        if nu not in [0.5, 1.5, 2.5]:
+            raise ValueError("nu parameter must be 0.5, 1.5, or 2.5")
+
+        l = torch.rand(input_dims)
+        sigma = torch.rand(1)
+
+        self.nu = nu
+        self.l = Parameter(l, lower=1e-6)
+        self.sigma = Parameter(sigma, lower=1e-6)
+
+    def K(self, X1, X2=None):
+        # X has shape (data_points,input_dims)
+        X1,X2 = self._check_input(X1,X2)
+        if X2 is None:
+            X2 = X1
+
+        dist = torch.abs(torch.tensordot(self.distance(X1,X2), 1.0/self.l(), dims=1))
+        if self.nu == 0.5:
+            constant = 1.0
+        elif self.nu == 1.5:
+            constant = 1.0 + np.sqrt(3.0)*dist
+        elif self.nu == 2.5:
+            constant = 1.0 + np.sqrt(5.0)*dist + 5.0/3.0*dist**2
+        return self.sigma()**2 * constant * torch.exp(-np.sqrt(self.nu*2.0)*dist)
    
+
    
+
    Ancestors
    
+
+
    Methods
    
+
    
+
    
+def K(self, X1, X2=None)
+
    
+
    
+
    
+
    
+
+Expand source code
+Browse git
+
+
    def K(self, X1, X2=None):
+    # X has shape (data_points,input_dims)
+    X1,X2 = self._check_input(X1,X2)
+    if X2 is None:
+        X2 = X1
+
+    dist = torch.abs(torch.tensordot(self.distance(X1,X2), 1.0/self.l(), dims=1))
+    if self.nu == 0.5:
+        constant = 1.0
+    elif self.nu == 1.5:
+        constant = 1.0 + np.sqrt(3.0)*dist
+    elif self.nu == 2.5:
+        constant = 1.0 + np.sqrt(5.0)*dist + 5.0/3.0*dist**2
+    return self.sigma()**2 * constant * torch.exp(-np.sqrt(self.nu*2.0)*dist)
    
+
    
+
    
+
    
+
    
+
    
 class PeriodicKernel
 (input_dims, active_dims=None, name='Periodic')
 
    
@@ -235,7 +338,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class PeriodicKernel(Kernel):
     def __init__(self, input_dims, active_dims=None, name="Periodic"):
@@ -259,11 +362,11 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def K(self, X1, X2=None)
 
    
 
    
@@ -271,7 +374,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def K(self, X1, X2=None):
     # X has shape (data_points,input_dims)
@@ -284,7 +387,7 @@ 
    Methods
    
 
    
 
    
 
    
-
    
+
    
 class PhiKernel
 (phi, input_dims, active_dims=None, name='Phi')
 
    
@@ -293,7 +396,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class PhiKernel(Kernel):
     def __init__(self, phi, input_dims, active_dims=None, name="Phi"):
@@ -317,11 +420,11 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def K(self, X1, X2=None)
 
    
 
    
@@ -329,7 +432,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def K(self, X1, X2=None):
     # X has shape (data_points,input_dims)
@@ -344,7 +447,7 @@ 
    Methods
    
 
    
 
    
 
-
    
+
    
 class PolynomialKernel
 (degree, input_dims=None, active_dims=None, name='Polynomial')
 
    
@@ -353,7 +456,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class PolynomialKernel(Kernel):
     def __init__(self, degree, input_dims=None, active_dims=None, name="Polynomial"):
@@ -374,11 +477,11 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def K(self, X1, X2=None)
 
    
 
    
@@ -386,7 +489,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def K(self, X1, X2=None):
     # X has shape (data_points,input_dims)
@@ -399,7 +502,7 @@ 
    Methods
    
 
    
 
    
 
-
    
+
    
 class RationalQuadraticKernel
 (alpha, input_dims, active_dims=None, name='RQ')
 
    
@@ -408,7 +511,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class RationalQuadraticKernel(Kernel):
     def __init__(self, alpha, input_dims, active_dims=None, name="RQ"):
@@ -431,11 +534,11 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def K(self, X1, X2=None)
 
    
 
    
@@ -443,7 +546,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def K(self, X1, X2=None):
     # X has shape (data_points,input_dims)
@@ -456,7 +559,7 @@ 
    Methods
    
 
    
 
    
 
-
    
+
    
 class SpectralKernel
 (input_dims, active_dims=None, name='SM')
 
    
@@ -465,7 +568,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class SpectralKernel(Kernel):
     def __init__(self, input_dims, active_dims=None, name="SM"):
@@ -490,11 +593,11 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def K(self, X1, X2=None)
 
    
 
    
@@ -502,7 +605,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def K(self, X1, X2=None):
     # X has shape (data_points,input_dims)
@@ -516,7 +619,7 @@ 
    Methods
    
 
    
 
    
 
-
    
+
    
 class SquaredExponentialKernel
 (input_dims, active_dims=None, name='SE')
 
    
@@ -525,7 +628,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class SquaredExponentialKernel(Kernel):
     def __init__(self, input_dims, active_dims=None, name="SE"):
@@ -547,11 +650,11 @@ 
    Methods
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def K(self, X1, X2=None)
 
    
 
    
@@ -559,7 +662,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def K(self, X1, X2=None):
     # X has shape (data_points,input_dims)
@@ -583,51 +686,57 @@ 
    Index
    
 
+
    Using the GPU
    
+
    If a GPU is available through CUDA it will be automatically used in tensor calculations, and may speed up training significantly. To get more information about whether CUDA is supported or which GPU is used, as well as more control over which CPU or GPU to use, see mogptk.gpr.config whose functions can be accessed through the main MOGPTK namespace (e.g. mogptk.print_gpu_information() or mogptk.use_gpu()).
    
 
    Advice on training
    
 
    Visualization and interpretation
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    """
 .. include:: ./documentation.md
@@ -75,18 +77,16 @@ 
    Visualization and interpretation
    
 from .serie import *
 from .data import *
 from .dataset import *
-
 from .model import *
-from .sm import *
-from .mosm import *
-from .csm import *
-from .conv import *
-from .sm_lmc import *
+from .models.sm import *
+from .models.mosm import *
+from .models.csm import *
+from .models.sm_lmc import *
+from .models.conv import *
 
 from .plot import *
 from .errors import *
-
-from .kernels.config import *
    
+from .gpr.config import *
    
 
    
 
 
    
@@ -96,14 +96,6 @@ 
    Sub-modules
    
 
    
 
    
 
    
-
    mogptk.conv
    
-
    
-
    
-
    
-
    mogptk.csm
    
-
    
-
    
-
    
 
    mogptk.data
    
 
    
 
    
@@ -116,7 +108,7 @@ 
    Sub-modules
    
 
    
 
    
 
    
-
    mogptk.kernels
    
+
    mogptk.gpr
    
 
    
 
    
 
    
@@ -124,7 +116,7 @@ 
    Sub-modules
    
 
    
 
    
 
    
-
    mogptk.mosm
    
+
    mogptk.models
    
 
    
 
    
 
    
@@ -136,14 +128,6 @@ 
    Sub-modules
    
 
    
 
    
 
    
-
    mogptk.sm
    
-
    
-
    
-
    
-
    mogptk.sm_lmc
    
-
    
-
    
-
    
 
    
 
 
    
@@ -163,6 +147,7 @@ 
    Index
    
 
  • Transformations
    • 
 
 
+
  • Using the GPU
    • 
 
  • Advice on training
    • 
 
  • Visualization and interpretation
    • 
 
@@ -171,18 +156,14 @@ 
    Index
    
 
  • Sub-modules
    
 
 
    • 
 
diff --git a/docs/model.html b/docs/model.html
index 110001ba..33866972 100644
--- a/docs/model.html
+++ b/docs/model.html
@@ -25,7 +25,7 @@ 
    Module mogptk.model
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    import os
 import time
@@ -39,7 +39,7 @@ 
    Module mogptk.model
    
 
 from .serie import Serie
 from .dataset import DataSet
-from .kernels import GPR, CholeskyException, Kernel, MultiOutputKernel, IndependentMultiOutputKernel
+from .gpr import GPR, CholeskyException, Kernel, MultiOutputKernel, IndependentMultiOutputKernel
 from .errors import mean_absolute_error, mean_absolute_percentage_error, root_mean_squared_error
 
 logger = logging.getLogger('mogptk')
@@ -72,9 +72,9 @@ 
    Module mogptk.model
    
 
         Args:
             dataset (mogptk.dataset.DataSet, mogptk.data.Data): `DataSet` with `Data` objects for all the channels. When a (list or dict of) `Data` object is passed, it will automatically be converted to a `DataSet`.
-            kernel (mogptk.kernels.kernel.Kernel): The kernel class.
+            kernel (mogptk.gpr.kernel.Kernel): The kernel class.
             model: Gaussian process model to use, such as `mogptk.model.Exact`.
-            mean (mogptk.kernels.mean.Mean): The mean class.
+            mean (mogptk.gpr.mean.Mean): The mean class.
             name (str): Name of the model.
         """
         
@@ -122,7 +122,7 @@ 
    Module mogptk.model
    
         Returns all parameters of the kernel.
 
         Returns:
-            list: mogptk.kernels.parameter.Parameter    
+            list: mogptk.gpr.parameter.Parameter
 
         Examples:
             >>> params = model.get_parameters()
@@ -496,7 +496,7 @@ 
    Module mogptk.model
    
             else:
                 X[m*n:(m+1)*n,1] = np.linspace(start[m], end[m], n)
 
-        return self.model.K(X).detach().numpy()
+        return self.model.K(X)
 
     def plot(self, start=None, end=None, n=31, title=None, figsize=(12,12)):
         """
@@ -592,7 +592,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def LoadModel(filename):
     """
@@ -622,7 +622,7 @@ 
    Classes
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class Exact:
     """
@@ -641,7 +641,7 @@ 
    Methods
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def build(self, kernel, x, y, mean=None, name=None):
     return GPR(kernel, x, y, mean=mean, name=name)
    
@@ -659,11 +659,11 @@ 
    Args
    
 
    
 
    dataset : DataSet, Data
    
 
    DataSet with Data objects for all the channels. When a (list or dict of) Data object is passed, it will automatically be converted to a DataSet.
    
-
    kernel : Kernel
    
+
    kernel : Kernel
    
 
    The kernel class.
    
 
    model
    
 
    Gaussian process model to use, such as Exact.
    
-
    mean : Mean
    
+
    mean : Mean
    
 
    The mean class.
    
 
    name : str
    
 
    Name of the model.
    
@@ -671,7 +671,7 @@ 
    Args
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class Model:
     def __init__(self, dataset, kernel, model=Exact(), mean=None, name=None):
@@ -680,9 +680,9 @@ 
    Args
    
 
         Args:
             dataset (mogptk.dataset.DataSet, mogptk.data.Data): `DataSet` with `Data` objects for all the channels. When a (list or dict of) `Data` object is passed, it will automatically be converted to a `DataSet`.
-            kernel (mogptk.kernels.kernel.Kernel): The kernel class.
+            kernel (mogptk.gpr.kernel.Kernel): The kernel class.
             model: Gaussian process model to use, such as `mogptk.model.Exact`.
-            mean (mogptk.kernels.mean.Mean): The mean class.
+            mean (mogptk.gpr.mean.Mean): The mean class.
             name (str): Name of the model.
         """
         
@@ -730,7 +730,7 @@ 
    Args
    
         Returns all parameters of the kernel.
 
         Returns:
-            list: mogptk.kernels.parameter.Parameter    
+            list: mogptk.gpr.parameter.Parameter
 
         Examples:
             >>> params = model.get_parameters()
@@ -1104,7 +1104,7 @@ 
    Args
    
             else:
                 X[m*n:(m+1)*n,1] = np.linspace(start[m], end[m], n)
 
-        return self.model.K(X).detach().numpy()
+        return self.model.K(X)
 
     def plot(self, start=None, end=None, n=31, title=None, figsize=(12,12)):
         """
@@ -1150,11 +1150,11 @@ 
    Args
    
 
    
 
    Subclasses
    
 
 
    Methods
    
 
    
@@ -1179,7 +1179,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def error(self, method='MAE'):
     """
@@ -1229,7 +1229,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def get_gram_matrix(self, start=None, end=None, n=31):
     """
@@ -1265,7 +1265,7 @@ 
    Examples
    
         else:
             X[m*n:(m+1)*n,1] = np.linspace(start[m], end[m], n)
 
-    return self.model.K(X).detach().numpy()
    
+    return self.model.K(X)
    
 
    
 
 
    
@@ -1276,8 +1276,7 @@ 
    Examples
    
 
    Returns
    
 
    
 
    list
    
-
    mogptk.kernels.parameter.Parameter
-
    
+
    mogptk.gpr.parameter.Parameter
    
 
    
 
    Examples
    
 
    >>> params = model.get_parameters()
@@ -1285,14 +1284,14 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def get_parameters(self):
     """
     Returns all parameters of the kernel.
 
     Returns:
-        list: mogptk.kernels.parameter.Parameter    
+        list: mogptk.gpr.parameter.Parameter
 
     Examples:
         >>> params = model.get_parameters()
@@ -1316,7 +1315,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def log_marginal_likelihood(self):
     """
@@ -1347,7 +1346,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def loss(self):
     """
@@ -1390,7 +1389,7 @@ 
    Returns
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def plot(self, start=None, end=None, n=31, title=None, figsize=(12,12)):
     """
@@ -1460,7 +1459,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def plot_prediction(self, title=None, figsize=None, legend=True, transformed=False):
     """
@@ -1506,7 +1505,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def predict(self, X=None, sigma=2.0, transformed=False):
     """
@@ -1575,7 +1574,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def print_parameters(self):
     """
@@ -1603,7 +1602,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def save(self, filename):
     """
@@ -1657,7 +1656,7 @@ 
    Examples
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def train(
     self,
diff --git a/docs/conv.html b/docs/models/conv.html
similarity index 86%
rename from docs/conv.html
rename to docs/models/conv.html
index 2c2e13a0..c14908b3 100644
--- a/docs/conv.html
+++ b/docs/models/conv.html
@@ -4,7 +4,7 @@
 
 
 
-mogptk.conv API documentation
+mogptk.models.conv API documentation
 
 
 
@@ -19,19 +19,19 @@
 
    
 
    
 
    
-
    Module mogptk.conv
    
+
    Module mogptk.models.conv
    
 
    
 
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    import numpy as np
 
-from .dataset import DataSet
-from .model import Model, Exact, logger
-from .kernels import GaussianConvolutionProcessKernel, MixtureKernel
+from ..dataset import DataSet
+from ..model import Model, Exact, logger
+from ..gpr import GaussianConvolutionProcessKernel, MixtureKernel
 
 class CONV(Model):
     """
@@ -41,13 +41,13 @@ 
    Module mogptk.conv
    
         dataset (mogptk.dataset.DataSet): `DataSet` object of data for all channels.
         Q (int): Number of components.
         model: Gaussian process model to use, such as `mogptk.model.Exact`.
-        mean (mogptk.kernels.mean.Mean): The mean class.
+        mean (mogptk.gpr.mean.Mean): The mean class.
         name (str): Name of the model.
 
     Attributes:
         dataset: The associated mogptk.dataset.DataSet.
-        model: The mogptk.kernels.model.Model.
-        kernel: The mogptk.kernels.kernel.Kernel.
+        model: The mogptk.gpr.model.Model.
+        kernel: The mogptk.gpr.kernel.Kernel.
 
     Examples:
 
@@ -152,7 +152,7 @@ 
    Module mogptk.conv
    
 
    
 
    Classes
    
 
    
-
    
+
    
 class CONV
 (dataset, Q=1, model=<mogptk.model.Exact object>, mean=None, name='CONV')
 
    
@@ -160,13 +160,13 @@ 
    Classes
    
 
    CONV is the Convolutional Gaussian kernel with Q components [1]. The parameters will be randomly instantiated, use init_parameters() to initialize the parameters to reasonable values for the current data set.
    
 
    Args
    
 
    
-
    dataset : DataSet
    
+
    dataset : DataSet
    
 
    DataSet object of data for all channels.
    
 
    Q : int
    
 
    Number of components.
    
 
    model
    
-
    Gaussian process model to use, such as Exact.
    
-
    mean : Mean
    
+
    Gaussian process model to use, such as Exact.
    
+
    mean : Mean
    
 
    The mean class.
    
 
    name : str
    
 
    Name of the model.
    
@@ -176,9 +176,9 @@ 
    Attributes
    
 
    dataset
    
 
    The associated mogptk.dataset.DataSet.
    
 
    model
    
-
    The mogptk.kernels.model.Model.
    
+
    The mogptk.gpr.model.Model.
    
 
    kernel
    
-
    The mogptk.kernels.kernel.Kernel.
    
+
    The mogptk.gpr.kernel.Kernel.
    
 
    
 
    Examples:
    
 
    >>> import numpy as np
@@ -199,13 +199,13 @@ 
    Attributes
    
 
    Model is the base class for multi-output Gaussian process models.
    
 
    Args
    
 
    
-
    dataset : DataSet, Data
    
+
    dataset : DataSet, Data
    
 
    DataSet with Data objects for all the channels. When a (list or dict of) Data object is passed, it will automatically be converted to a DataSet.
    
-
    kernel : Kernel
    
+
    kernel : Kernel
    
 
    The kernel class.
    
 
    model
    
-
    Gaussian process model to use, such as Exact.
    
-
    mean : Mean
    
+
    Gaussian process model to use, such as Exact.
    
+
    mean : Mean
    
 
    The mean class.
    
 
    name : str
    
 
    Name of the model.
    
@@ -213,7 +213,7 @@ 
    Args
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    class CONV(Model):
     """
@@ -223,13 +223,13 @@ 
    Args
    
         dataset (mogptk.dataset.DataSet): `DataSet` object of data for all channels.
         Q (int): Number of components.
         model: Gaussian process model to use, such as `mogptk.model.Exact`.
-        mean (mogptk.kernels.mean.Mean): The mean class.
+        mean (mogptk.gpr.mean.Mean): The mean class.
         name (str): Name of the model.
 
     Attributes:
         dataset: The associated mogptk.dataset.DataSet.
-        model: The mogptk.kernels.model.Model.
-        kernel: The mogptk.kernels.kernel.Kernel.
+        model: The mogptk.gpr.model.Model.
+        kernel: The mogptk.gpr.kernel.Kernel.
 
     Examples:
 
@@ -326,11 +326,11 @@ 
    Args
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
+
    
 def init_parameters(self, method='SM', sm_init='BNSE', sm_method='Adam', sm_iters=100, sm_params={}, sm_plot=False)
 
    
 
    
@@ -359,7 +359,7 @@ 
    Args
    
 
    
 
 Expand source code
-Browse git
+Browse git
 
 
    def init_parameters(self, method='SM', sm_init='BNSE', sm_method='Adam', sm_iters=100, sm_params={}, sm_plot=False):
     """
@@ -421,19 +421,19 @@ 
    Args
    
 
    
 
    Inherited members
    
 
@@ -449,15 +449,15 @@ 
    Index
    
 
      
 
    • Super-module
      
 
 
      • 
 
    • Classes
      
 
diff --git a/docs/csm.html b/docs/models/csm.html
similarity index 87%
rename from docs/csm.html
rename to docs/models/csm.html
index dc39af11..2d0f1e35 100644
--- a/docs/csm.html
+++ b/docs/models/csm.html
@@ -4,7 +4,7 @@
 
 
 
-mogptk.csm API documentation
+mogptk.models.csm API documentation
 
 
 
@@ -19,19 +19,19 @@
 
      
 
      
 
      
-
      Module mogptk.csm
      
+
      Module mogptk.models.csm
      
 
      
 
      
 
      
 
 Expand source code
-Browse git
+Browse git
 
 
      import numpy as np
 
-from .dataset import DataSet
-from .model import Model, Exact, logger
-from .kernels import CrossSpectralKernel, MixtureKernel
+from ..dataset import DataSet
+from ..model import Model, Exact, logger
+from ..gpr import CrossSpectralKernel, MixtureKernel
 
 class CSM(Model):
     """
@@ -42,13 +42,13 @@ 
      Module mogptk.csm
      
         Q (int): Number of components.
         Rq (int): Number of subcomponents.
         model: Gaussian process model to use, such as `mogptk.model.Exact`.
-        mean (mogptk.kernels.mean.Mean): The mean class.
+        mean (mogptk.gpr.mean.Mean): The mean class.
         name (str): Name of the model.
 
     Attributes:
         dataset: The associated mogptk.dataset.DataSet.
-        model: The mogptk.kernels.model.Model.
-        kernel: The mogptk.kernels.kernel.Kernel.
+        model: The mogptk.gpr.model.Model.
+        kernel: The mogptk.gpr.kernel.Kernel.
 
     Examples:
 
@@ -160,7 +160,7 @@ 
      Module mogptk.csm
      
 
      
 
      Classes
      
 
      
-
      
+
      
 class CSM
 (dataset, Q=1, Rq=1, model=<mogptk.model.Exact object>, mean=None, name='CSM')
 
      
@@ -168,15 +168,15 @@ 
      Classes
      
 
      Cross Spectral Mixture kernel [1] with Q components and Rq latent functions. The parameters will be randomly instantiated, use init_parameters() to initialize the parameters to reasonable values for the current data set.
      
 
      Args
      
 
      
-
      dataset : DataSet
      
+
      dataset : DataSet
      
 
      DataSet object of data for all channels.
      
 
      Q : int
      
 
      Number of components.
      
 
      Rq : int
      
 
      Number of subcomponents.
      
 
      model
      
-
      Gaussian process model to use, such as Exact.
      
-
      mean : Mean
      
+
      Gaussian process model to use, such as Exact.
      
+
      mean : Mean
      
 
      The mean class.
      
 
      name : str
      
 
      Name of the model.
      
@@ -186,9 +186,9 @@ 
      Attributes
      
 
      dataset
      
 
      The associated mogptk.dataset.DataSet.
      
 
      model
      
-
      The mogptk.kernels.model.Model.
      
+
      The mogptk.gpr.model.Model.
      
 
      kernel
      
-
      The mogptk.kernels.kernel.Kernel.
      
+
      The mogptk.gpr.kernel.Kernel.
      
 
      
 
      Examples:
      
 
      >>> import numpy as np
@@ -209,13 +209,13 @@ 
      Attributes
      
 
      Model is the base class for multi-output Gaussian process models.
      
 
      Args
      
 
      
-
      dataset : DataSet, Data
      
+
      dataset : DataSet, Data
      
 
      DataSet with Data objects for all the channels. When a (list or dict of) Data object is passed, it will automatically be converted to a DataSet.
      
-
      kernel : Kernel
      
+
      kernel : Kernel
      
 
      The kernel class.
      
 
      model
      
-
      Gaussian process model to use, such as Exact.
      
-
      mean : Mean
      
+
      Gaussian process model to use, such as Exact.
      
+
      mean : Mean
      
 
      The mean class.
      
 
      name : str
      
 
      Name of the model.
      
@@ -223,7 +223,7 @@ 
      Args
      
 
      
 
 Expand source code
-Browse git
+Browse git
 
 
      class CSM(Model):
     """
@@ -234,13 +234,13 @@ 
      Args
      
         Q (int): Number of components.
         Rq (int): Number of subcomponents.
         model: Gaussian process model to use, such as `mogptk.model.Exact`.
-        mean (mogptk.kernels.mean.Mean): The mean class.
+        mean (mogptk.gpr.mean.Mean): The mean class.
         name (str): Name of the model.
 
     Attributes:
         dataset: The associated mogptk.dataset.DataSet.
-        model: The mogptk.kernels.model.Model.
-        kernel: The mogptk.kernels.kernel.Kernel.
+        model: The mogptk.gpr.model.Model.
+        kernel: The mogptk.gpr.kernel.Kernel.
 
     Examples:
 
@@ -344,11 +344,11 @@ 
      Args
      
 
      
 
      Ancestors
      
 
 
      Methods
      
 
      
-
      
+
      
 def init_parameters(self, method='BNSE', sm_init='BNSE', sm_method='Adam', sm_iters=100, sm_params={}, sm_plot=False)
 
      
 
      
@@ -377,7 +377,7 @@ 
      Args
      
 
      
 
 Expand source code
-Browse git
+Browse git
 
 
      def init_parameters(self, method='BNSE', sm_init='BNSE', sm_method='Adam', sm_iters=100, sm_params={}, sm_plot=False):
     """
@@ -449,19 +449,19 @@ 
      Args
      
 
      
 
      Inherited members
      
 
@@ -477,15 +477,15 @@ 
      Index
      
 
        
 
      • Super-module
        
 
 
        • 
 
      • Classes
        
 
diff --git a/docs/models/index.html b/docs/models/index.html
new file mode 100644
index 00000000..1986054f
--- /dev/null
+++ b/docs/models/index.html
@@ -0,0 +1,87 @@
+
+
+
+
+
+
+mogptk.models API documentation
+
+
+
+
+
+
+
+
+
+
+
+
        
+
+
+
        
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/mosm.html b/docs/models/mosm.html
similarity index 89%
rename from docs/mosm.html
rename to docs/models/mosm.html
index f255666a..49ed1c9a 100644
--- a/docs/mosm.html
+++ b/docs/models/mosm.html
@@ -4,7 +4,7 @@
 
 
 
-mogptk.mosm API documentation
+mogptk.models.mosm API documentation
 
 
 
@@ -19,21 +19,21 @@
 
        
 
        
 
        
-
        Module mogptk.mosm
        
+
        Module mogptk.models.mosm
        
 
        
 
        
 
        
 
 Expand source code
-Browse git
+Browse git
 
 
        import numpy as np
 import matplotlib.pyplot as plt
 
-from .dataset import DataSet
-from .model import Model, Exact, logger
-from .kernels import MultiOutputSpectralKernel, MixtureKernel
-from .plot import plot_spectrum
+from ..dataset import DataSet
+from ..model import Model, Exact, logger
+from ..gpr import MultiOutputSpectralKernel, MixtureKernel
+from ..plot import plot_spectrum
 
 class MOSM(Model):
     """
@@ -43,13 +43,13 @@ 
        Module mogptk.mosm
        
         dataset (mogptk.dataset.DataSet): `DataSet` object of data for all channels.
         Q (int): Number of components.
         model: Gaussian process model to use, such as `mogptk.model.Exact`.
-        mean (mogptk.kernels.mean.Mean): The mean class.
+        mean (mogptk.gpr.mean.Mean): The mean class.
         name (str): Name of the model.
 
     Atributes:
         dataset: The associated mogptk.dataset.DataSet.
-        model: The mogptk.kernels.model.Model.
-        kernel: The mogptk.kernels.kernel.Kernel.
+        model: The mogptk.gpr.model.Model.
+        kernel: The mogptk.gpr.kernel.Kernel.
 
     Examples:
     >>> import numpy as np
@@ -312,7 +312,7 @@ 
        Module mogptk.mosm
        
 
        
 
        Classes
        
 
        
-
        
+
        
 class MOSM
 (dataset, Q=1, model=<mogptk.model.Exact object>, mean=None, name='MOSM')
 
        
@@ -320,13 +320,13 @@ 
        Classes
        
 
        Multi-Output Spectral Mixture kernel with Q components as proposed by [1]. The parameters will be randomly instantiated, use init_parameters() to initialize the parameters to reasonable values for the current data set.
        
 
        Args
        
 
        
-
        dataset : DataSet
        
+
        dataset : DataSet
        
 
        DataSet object of data for all channels.
        
 
        Q : int
        
 
        Number of components.
        
 
        model
        
-
        Gaussian process model to use, such as Exact.
        
-
        mean : Mean
        
+
        Gaussian process model to use, such as Exact.
        
+
        mean : Mean
        
 
        The mean class.
        
 
        name : str
        
 
        Name of the model.
        
@@ -336,9 +336,9 @@ 
        Atributes
        
 
        dataset
        
 
        The associated mogptk.dataset.DataSet.
        
 
        model
        
-
        The mogptk.kernels.model.Model.
        
+
        The mogptk.gpr.model.Model.
        
 
        kernel
        
-
        The mogptk.kernels.kernel.Kernel.
        
+
        The mogptk.gpr.kernel.Kernel.
        
 
        
 
        Examples:
        
 
        >>> import numpy as np
@@ -359,13 +359,13 @@ 
        Atributes
        
 
        Model is the base class for multi-output Gaussian process models.
        
 
        Args
        
 
        
-
        dataset : DataSet, Data
        
+
        dataset : DataSet, Data
        
 
        DataSet with Data objects for all the channels. When a (list or dict of) Data object is passed, it will automatically be converted to a DataSet.
        
-
        kernel : Kernel
        
+
        kernel : Kernel
        
 
        The kernel class.
        
 
        model
        
-
        Gaussian process model to use, such as Exact.
        
-
        mean : Mean
        
+
        Gaussian process model to use, such as Exact.
        
+
        mean : Mean
        
 
        The mean class.
        
 
        name : str
        
 
        Name of the model.
        
@@ -373,7 +373,7 @@ 
        Args
        
 
        
 
 Expand source code
-Browse git
+Browse git
 
 
        class MOSM(Model):
     """
@@ -383,13 +383,13 @@ 
        Args
        
         dataset (mogptk.dataset.DataSet): `DataSet` object of data for all channels.
         Q (int): Number of components.
         model: Gaussian process model to use, such as `mogptk.model.Exact`.
-        mean (mogptk.kernels.mean.Mean): The mean class.
+        mean (mogptk.gpr.mean.Mean): The mean class.
         name (str): Name of the model.
 
     Atributes:
         dataset: The associated mogptk.dataset.DataSet.
-        model: The mogptk.kernels.model.Model.
-        kernel: The mogptk.kernels.kernel.Kernel.
+        model: The mogptk.gpr.model.Model.
+        kernel: The mogptk.gpr.kernel.Kernel.
 
     Examples:
     >>> import numpy as np
@@ -644,11 +644,11 @@ 
        Args
        
 
        
 
        Ancestors
        
 
 
        Methods
        
 
        
-
        
+
        
 def check(self)
 
        
 
        
@@ -656,7 +656,7 @@ 
        Methods
        
 
        
 
 Expand source code
-Browse git
+Browse git
 
 
        def check(self):
     """
@@ -670,7 +670,7 @@ 
        Methods
        
                 print("‣ MOSM approaches RBF kernel for q=%d in channel='%s'" % (q, self.dataset[j].name))
        
 
        
 
        
-
        
+
        
 def init_parameters(self, method='BNSE', sm_init='BNSE', sm_method='Adam', sm_iters=100, sm_params={}, sm_plot=False)
 
        
 
        
@@ -699,7 +699,7 @@ 
        Args
        
 
        
 
 Expand source code
-Browse git
+Browse git
 
 
        def init_parameters(self, method='BNSE', sm_init='BNSE', sm_method='Adam', sm_iters=100, sm_params={}, sm_plot=False):
     """
@@ -766,7 +766,7 @@ 
        Args
        
         self.model.kernel[q].noise.assign(noise)
        
 
        
 
        
-
        
+
        
 def plot_cross_spectrum(self, title=None, figsize=(12, 12))
 
        
 
        
@@ -774,7 +774,7 @@ 
        Args
        
 
        
 
 Expand source code
-Browse git
+Browse git
 
 
        def plot_cross_spectrum(self, title=None, figsize=(12,12)):
     """
@@ -808,7 +808,7 @@ 
        Args
        
     return fig, axes
        
 
        
 
        
-
        
+
        
 def plot_spectrum(self, title=None)
 
        
 
        
@@ -816,7 +816,7 @@ 
        Args
        
 
        
 
 Expand source code
-Browse git
+Browse git
 
 
        def plot_spectrum(self, title=None):
     """
@@ -835,19 +835,19 @@ 
        Args
        
 
        
 
        Inherited members
        
 
@@ -863,18 +863,18 @@ 
        Index
        
 
          
 
        • Super-module
          
 
 
          • 
 
        • Classes
          
 
diff --git a/docs/sm.html b/docs/models/sm.html
similarity index 87%
rename from docs/sm.html
rename to docs/models/sm.html
index 858b3d7c..b653f4a0 100644
--- a/docs/sm.html
+++ b/docs/models/sm.html
@@ -4,7 +4,7 @@
 
 
 
-mogptk.sm API documentation
+mogptk.models.sm API documentation
 
 
 
@@ -19,20 +19,20 @@
 
          
 
          
 
          
-
          Module mogptk.sm
          
+
          Module mogptk.models.sm
          
 
          
 
          
 
          
 
 Expand source code
-Browse git
+Browse git
 
 
          import numpy as np
 
-from .dataset import DataSet
-from .model import Model, Exact, logger
-from .kernels import SpectralKernel, IndependentMultiOutputKernel, MixtureKernel
-from .plot import plot_spectrum
+from ..dataset import DataSet
+from ..model import Model, Exact, logger
+from ..gpr import SpectralKernel, IndependentMultiOutputKernel, MixtureKernel
+from ..plot import plot_spectrum
 
 class SM(Model):
     """
@@ -42,13 +42,13 @@ 
          Module mogptk.sm
          
         dataset (mogptk.dataset.DataSet): `DataSet` object of data for all channels.
         Q (int): Number of components.
         model: Gaussian process model to use, such as `mogptk.model.Exact`.
-        mean (mogptk.kernels.mean.Mean): The mean class.
+        mean (mogptk.gpr.mean.Mean): The mean class.
         name (str): Name of the model.
 
     Attributes:
         dataset: The associated mogptk.dataset.DataSet.
-        model: The mogptk.kernels.model.Model.
-        kernel: The mogptk.kernels.kernel.Kernel.
+        model: The mogptk.gpr.model.Model.
+        kernel: The mogptk.gpr.kernel.Kernel.
 
     Examples:
 
@@ -182,7 +182,7 @@ 
          Module mogptk.sm
          
 
          
 
          Classes
          
 
          
-
          
+
          
 class SM
 (dataset, Q=1, model=<mogptk.model.Exact object>, mean=None, name='SM')
 
          
@@ -190,13 +190,13 @@ 
          Classes
          
 
          Independent Spectral Mixture kernels per channel. The spectral mixture kernel is proposed by [1]. The parameters will be randomly instantiated, use init_parameters() to initialize the parameters to reasonable values for the current data set.
          
 
          Args
          
 
          
-
          dataset : DataSet
          
+
          dataset : DataSet
          
 
          DataSet object of data for all channels.
          
 
          Q : int
          
 
          Number of components.
          
 
          model
          
-
          Gaussian process model to use, such as Exact.
          
-
          mean : Mean
          
+
          Gaussian process model to use, such as Exact.
          
+
          mean : Mean
          
 
          The mean class.
          
 
          name : str
          
 
          Name of the model.
          
@@ -206,9 +206,9 @@ 
          Attributes
          
 
          dataset
          
 
          The associated mogptk.dataset.DataSet.
          
 
          model
          
-
          The mogptk.kernels.model.Model.
          
+
          The mogptk.gpr.model.Model.
          
 
          kernel
          
-
          The mogptk.kernels.kernel.Kernel.
          
+
          The mogptk.gpr.kernel.Kernel.
          
 
          
 
          Examples:
          
 
          >>> import numpy as np
@@ -228,13 +228,13 @@ 
          Attributes
          
 
          Model is the base class for multi-output Gaussian process models.
          
 
          Args
          
 
          
-
          dataset : DataSet, Data
          
+
          dataset : DataSet, Data
          
 
          DataSet with Data objects for all the channels. When a (list or dict of) Data object is passed, it will automatically be converted to a DataSet.
          
-
          kernel : Kernel
          
+
          kernel : Kernel
          
 
          The kernel class.
          
 
          model
          
-
          Gaussian process model to use, such as Exact.
          
-
          mean : Mean
          
+
          Gaussian process model to use, such as Exact.
          
+
          mean : Mean
          
 
          The mean class.
          
 
          name : str
          
 
          Name of the model.
          
@@ -242,7 +242,7 @@ 
          Args
          
 
          
 
 Expand source code
-Browse git
+Browse git
 
 
          class SM(Model):
     """
@@ -252,13 +252,13 @@ 
          Args
          
         dataset (mogptk.dataset.DataSet): `DataSet` object of data for all channels.
         Q (int): Number of components.
         model: Gaussian process model to use, such as `mogptk.model.Exact`.
-        mean (mogptk.kernels.mean.Mean): The mean class.
+        mean (mogptk.gpr.mean.Mean): The mean class.
         name (str): Name of the model.
 
     Attributes:
         dataset: The associated mogptk.dataset.DataSet.
-        model: The mogptk.kernels.model.Model.
-        kernel: The mogptk.kernels.kernel.Kernel.
+        model: The mogptk.gpr.model.Model.
+        kernel: The mogptk.gpr.kernel.Kernel.
 
     Examples:
 
@@ -384,11 +384,11 @@ 
          Args
          
 
          
 
          Ancestors
          
 
 
          Methods
          
 
          
-
          
+
          
 def init_parameters(self, method='BNSE')
 
          
 
          
@@ -407,7 +407,7 @@ 
          Args
          
 
          
 
 Expand source code
-Browse git
+Browse git
 
 
          def init_parameters(self, method='BNSE'):
     """
@@ -488,7 +488,7 @@ 
          Args
          
             self.model.kernel[j][q].variance.assign(variances[j][q,:])
          
 
          
 
          
-
          
+
          
 def plot_spectrum(self, title=None)
 
          
 
          
@@ -496,7 +496,7 @@ 
          Args
          
 
          
 
 Expand source code
-Browse git
+Browse git
 
 
          def plot_spectrum(self, title=None):
     """
@@ -516,19 +516,19 @@ 
          Args
          
 
          
 
          Inherited members
          
 
@@ -544,16 +544,16 @@ 
          Index
          
 
            
 
          • Super-module
            
 
 
            • 
 
          • Classes
            
 
diff --git a/docs/sm_lmc.html b/docs/models/sm_lmc.html
similarity index 87%
rename from docs/sm_lmc.html
rename to docs/models/sm_lmc.html
index 7a67e786..0081b2cc 100644
--- a/docs/sm_lmc.html
+++ b/docs/models/sm_lmc.html
@@ -4,7 +4,7 @@
 
 
 
-mogptk.sm_lmc API documentation
+mogptk.models.sm_lmc API documentation
 
 
 
@@ -19,19 +19,19 @@
 
            
 
            
 
            
-
            Module mogptk.sm_lmc
            
+
            Module mogptk.models.sm_lmc
            
 
            
 
            
 
            
 
 Expand source code
-Browse git
+Browse git
 
 
            import numpy as np
 
-from .dataset import DataSet
-from .model import Model, Exact, logger
-from .kernels import LinearModelOfCoregionalizationKernel, SpectralKernel
+from ..dataset import DataSet
+from ..model import Model, Exact, logger
+from ..gpr import LinearModelOfCoregionalizationKernel, SpectralKernel
 
 class SM_LMC(Model):
     """
@@ -42,13 +42,13 @@ 
            Module mogptk.sm_lmc
            
         Q (int): Number of components.
         Rq (int): Number of subcomponents.
         model: Gaussian process model to use, such as `mogptk.model.Exact`.
-        mean (mogptk.kernels.mean.Mean): The mean class.
+        mean (mogptk.gpr.mean.Mean): The mean class.
         name (str): Name of the model.
 
     Attributes:
         dataset: The associated mogptk.dataset.DataSet.
-        model: The mogptk.kernels.model.Model.
-        kernel: The mogptk.kernels.kernel.Kernel.
+        model: The mogptk.gpr.model.Model.
+        kernel: The mogptk.gpr.kernel.Kernel.
 
     Examples:
 
@@ -162,7 +162,7 @@ 
            Module mogptk.sm_lmc
            
 
            
 
            Classes
            
 
            
-
            
+
            
 class SM_LMC
 (dataset, Q=1, Rq=1, model=<mogptk.model.Exact object>, mean=None, name='SM-LMC')
 
            
@@ -170,15 +170,15 @@ 
            Classes
            
 
            Spectral Mixture Linear Model of Coregionalization kernel with Q components and Rq latent functions. The SM kernel as proposed by [1] is combined with the LMC kernel as proposed by [2]. The parameters will be randomly instantiated, use init_parameters() to initialize the parameters to reasonable values for the current data set.
            
 
            Args
            
 
            
-
            dataset : DataSet
            
+
            dataset : DataSet
            
 
            DataSet object of data for all channels.
            
 
            Q : int
            
 
            Number of components.
            
 
            Rq : int
            
 
            Number of subcomponents.
            
 
            model
            
-
            Gaussian process model to use, such as Exact.
            
-
            mean : Mean
            
+
            Gaussian process model to use, such as Exact.
            
+
            mean : Mean
            
 
            The mean class.
            
 
            name : str
            
 
            Name of the model.
            
@@ -188,9 +188,9 @@ 
            Attributes
            
 
            dataset
            
 
            The associated mogptk.dataset.DataSet.
            
 
            model
            
-
            The mogptk.kernels.model.Model.
            
+
            The mogptk.gpr.model.Model.
            
 
            kernel
            
-
            The mogptk.kernels.kernel.Kernel.
            
+
            The mogptk.gpr.kernel.Kernel.
            
 
            
 
            Examples:
            
 
            >>> import numpy as np
@@ -212,13 +212,13 @@ 
            Attributes
            
 
            Model is the base class for multi-output Gaussian process models.
            
 
            Args
            
 
            
-
            dataset : DataSet, Data
            
+
            dataset : DataSet, Data
            
 
            DataSet with Data objects for all the channels. When a (list or dict of) Data object is passed, it will automatically be converted to a DataSet.
            
-
            kernel : Kernel
            
+
            kernel : Kernel
            
 
            The kernel class.
            
 
            model
            
-
            Gaussian process model to use, such as Exact.
            
-
            mean : Mean
            
+
            Gaussian process model to use, such as Exact.
            
+
            mean : Mean
            
 
            The mean class.
            
 
            name : str
            
 
            Name of the model.
            
@@ -226,7 +226,7 @@ 
            Args
            
 
            
 
 Expand source code
-Browse git
+Browse git
 
 
            class SM_LMC(Model):
     """
@@ -237,13 +237,13 @@ 
            Args
            
         Q (int): Number of components.
         Rq (int): Number of subcomponents.
         model: Gaussian process model to use, such as `mogptk.model.Exact`.
-        mean (mogptk.kernels.mean.Mean): The mean class.
+        mean (mogptk.gpr.mean.Mean): The mean class.
         name (str): Name of the model.
 
     Attributes:
         dataset: The associated mogptk.dataset.DataSet.
-        model: The mogptk.kernels.model.Model.
-        kernel: The mogptk.kernels.kernel.Kernel.
+        model: The mogptk.gpr.model.Model.
+        kernel: The mogptk.gpr.kernel.Kernel.
 
     Examples:
 
@@ -349,11 +349,11 @@ 
            Args
            
 
            
 
            Ancestors
            
 
 
            Methods
            
 
            
-
            
+
            
 def init_parameters(self, method='BNSE', sm_init='BNSE', sm_method='Adam', sm_iters=100, sm_params={}, sm_plot=False)
 
            
 
            
@@ -382,7 +382,7 @@ 
            Args
            
 
            
 
 Expand source code
-Browse git
+Browse git
 
 
            def init_parameters(self, method='BNSE', sm_init='BNSE', sm_method='Adam', sm_iters=100, sm_params={}, sm_plot=False):
     """
@@ -451,19 +451,19 @@ 
            Args
            
 
            
 
            Inherited members
            
 
@@ -479,15 +479,15 @@ 
            Index
            
 
              
 
            • Super-module
              
 
 
              • 
 
            • Classes
              
 
diff --git a/docs/plot.html b/docs/plot.html
index 6aba7416..b297a02a 100644
--- a/docs/plot.html
+++ b/docs/plot.html
@@ -25,7 +25,7 @@ 
              Module mogptk.plot
              
 
              
 
 Expand source code
-Browse git
+Browse git
 
 
              import numpy as np
 import matplotlib.pyplot as plt
@@ -118,7 +118,7 @@ 
              Functions
              
 
              
 
 Expand source code
-Browse git
+Browse git
 
 
              def plot_spectrum(means, scales, weights=None, nyquist=None, titles=None, show=True, filename=None, title=None):
     """
diff --git a/docs/serie.html b/docs/serie.html
index 54d0bd42..6e320c47 100644
--- a/docs/serie.html
+++ b/docs/serie.html
@@ -25,7 +25,7 @@ 
              Module mogptk.serie
              
 
              
 
 Expand source code
-Browse git
+Browse git
 
 
              import numpy as np
 
@@ -248,7 +248,7 @@ 
              Classes
              
 
              
 
 Expand source code
-Browse git
+Browse git
 
 
              class Serie(np.ndarray):
     """
@@ -345,7 +345,7 @@ 
              Methods
              
 
              
 
 Expand source code
-Browse git
+Browse git
 
 
              def apply(self, transformers, x=None):
     if not isinstance(transformers, list):
@@ -368,7 +368,7 @@ 
              Methods
              
 
              
 
 Expand source code
-Browse git
+Browse git
 
 
              def detransform(self, array, x=None):
     for t in self.transformers[::-1]:
@@ -384,7 +384,7 @@ 
              Methods
              
 
              
 
 Expand source code
-Browse git
+Browse git
 
 
              def get_time_unit(self):
     if not self.is_datetime64():
@@ -405,7 +405,7 @@ 
              Methods
              
 
              
 
 Expand source code
-Browse git
+Browse git
 
 
              def is_datetime64(self):
     return np.issubdtype(self.dtype, np.datetime64)
              
@@ -419,7 +419,7 @@ 
              Methods
              
 
              
 
 Expand source code
-Browse git
+Browse git
 
 
              def transform(self, array, x=None):
     array = array.astype(self.dtype).astype(np.float64)
@@ -438,7 +438,7 @@ 
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              class TransformBase:
     """
@@ -472,7 +472,7 @@ 
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              def backward(self, y, x=None):
     raise NotImplementedError
              
@@ -486,7 +486,7 @@ 
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              def forward(self, y, x=None):
     raise NotImplementedError
              
@@ -500,7 +500,7 @@ 
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              def set_data(self, data):
     pass
              
@@ -522,7 +522,7 @@ 
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              class TransformDetrend(TransformBase):
     """
@@ -568,7 +568,7 @@ 
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              def backward(self, y, x):
     if x is None:
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              def forward(self, y, x):
     if x is None:
@@ -602,7 +602,7 @@ 
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              def set_data(self, data):
     if data.get_input_dims() != 1:
@@ -623,7 +623,7 @@ 
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              class TransformLinear(TransformBase):
     """
@@ -656,7 +656,7 @@ 
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              def backward(self, y, x=None):
     return self.bias + self.slope*y
              
@@ -670,7 +670,7 @@ 
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              def forward(self, y, x=None):
     return (y-self.bias)/self.slope
              
@@ -684,7 +684,7 @@ 
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              def set_data(self, data):
     pass
              
@@ -700,7 +700,7 @@ 
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              class TransformLog(TransformBase):
     """
@@ -733,7 +733,7 @@ 
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              def backward(self, y, x=None):
     return np.exp(y + self.mean) - self.shift
              
@@ -747,7 +747,7 @@ 
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              def forward(self, y, x=None):
     return np.log(y + self.shift) - self.mean
              
@@ -761,7 +761,7 @@ 
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              def set_data(self, data):
     self.shift = 1 - data.Y.transformed.min()
@@ -778,7 +778,7 @@ 
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              class TransformNormalize(TransformBase):
     """
@@ -811,7 +811,7 @@ 
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              def backward(self, y, x=None):
     return (y+1.0)/2.0*(self.ymax-self.ymin)+self.ymin
              
@@ -825,7 +825,7 @@ 
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              def forward(self, y, x=None):
     return -1.0 + 2.0*(y-self.ymin)/(self.ymax-self.ymin)
              
@@ -839,7 +839,7 @@ 
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              def set_data(self, data):
     self.ymin = np.amin(data.Y.transformed[data.mask])
@@ -856,7 +856,7 @@ 
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              class TransformStandard(TransformBase):
     """
@@ -890,7 +890,7 @@ 
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              def backward(self, y, x=None):
     return (y * self.std) + self.mean
              
@@ -904,7 +904,7 @@ 
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              def forward(self, y, x=None):
     return (y - self.mean) / self.std
              
@@ -918,7 +918,7 @@ 
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              def set_data(self, data):
     # take only the non-removed observations
diff --git a/setup.py b/setup.py
index ff3bb5f6..d69b3997 100644
--- a/setup.py
+++ b/setup.py
@@ -16,7 +16,7 @@
     long_description = f.read()
 
 setup(name='mogptk',
-      version='0.2.1',
+      version='0.2.2',
       description='Multi-Output Gaussian Process ToolKit',
       long_description=long_description,
       long_description_content_type='text/markdown',
@@ -24,7 +24,7 @@
       author='Taco de Wolff, Alejandro Cuevas, Felipe Tobar',
       author_email='tacodewolff@gmail.com',
       license='MIT',
-      packages=['mogptk', 'mogptk.kernels'],
+      packages=['mogptk', 'mogptk.gpr', 'mogptk.models'],
       keywords=['MOGP', 'MOSM', 'GP', 'Gaussian Process', 'Multi-Output', 'Tobar', 'Parra'],
       python_requires='>=3.6',
       install_requires=requirements,