Added sphinx docs

.gitignore

@@ -6,7 +6,8 @@
 *.so.*
 *.sm
 *.out
+*__pycache__*
 .DS_Store
 smt/src/rbf/rbfclib.cpp
 smt/src/idw/idwclib.cpp
-smt/src/rmts/rmtsclib.cpp
+smt/src/rmts/rmtsclib.cpp

doc2/Makefile

@@ -0,0 +1,21 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SPHINXPROJ    = SMT
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	python preprocess.py
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

doc2/_src_docs/methods.rst

@@ -0,0 +1,15 @@
+Methods
+=======
+
+Placeholder text.
+
+.. toctree::
+   :maxdepth: 1
+
+   methods/rbf
+   methods/idw
+   methods/rmtb
+   methods/rmtc
+   methods/ls
+   methods/pa2
+   methods/krg

doc2/_src_docs/methods/idw.rst

@@ -0,0 +1,128 @@
+Inverse-distance weighting
+==========================
+
+.. code-block:: python
+
+  import numpy as np
+  import matplotlib.pyplot as plt
+  
+  from smt.methods import IDW
+  
+  xt = np.array([0., 1., 2., 3., 4.])
+  yt = np.array([0., 1., 1.5, 0.5, 1.0])
+  
+  sm = IDW(p=2)
+  sm.set_training_values(xt, yt)
+  sm.train()
+  
+  num = 100
+  x = np.linspace(0., 4., num)
+  y = sm.predict_values(x)
+  
+  plt.plot(xt, yt, 'o')
+  plt.plot(x, y)
+  plt.xlabel('x')
+  plt.ylabel('x')
+  plt.legend(['Training data', 'Prediction'])
+  plt.show()
+  
+.. plot::
+
+  import numpy as np
+  import matplotlib.pyplot as plt
+
+  from smt.methods import IDW
+
+  xt = np.array([0., 1., 2., 3., 4.])
+  yt = np.array([0., 1., 1.5, 0.5, 1.0])
+
+  sm = IDW(p=2)
+  sm.set_training_values(xt, yt)
+  sm.train()
+
+  num = 100
+  x = np.linspace(0., 4., num)
+  y = sm.predict_values(x)
+
+  plt.plot(xt, yt, 'o')
+  plt.plot(x, y)
+  plt.xlabel('x')
+  plt.ylabel('x')
+  plt.legend(['Training data', 'Prediction'])
+  plt.show()
+
+::
+
+  ___________________________________________________________________________
+     
+                                      IDW
+  ___________________________________________________________________________
+     
+   Problem size
+     
+        # training points.        : 5
+     
+  ___________________________________________________________________________
+     
+   Training
+     
+     Training ...
+     Training - done. Time (sec):  0.0001831
+  ___________________________________________________________________________
+     
+   Evaluation
+     
+        # eval points. : 100
+     
+     Predicting ...
+     Predicting - done. Time (sec):  0.0000508
+     
+     Prediction time/pt. (sec) :  0.0000005
+     
+  
+
+.. list-table:: List of options
+  :header-rows: 1
+  :widths: 15, 10, 20, 20, 30
+  :stub-columns: 0
+
+  *  -  Option
+     -  Default
+     -  Acceptable values
+     -  Acceptable values
+     -  Description
+  *  -  print_global
+     -  True
+     -  [None]
+     -  ['bool']
+     -  Global print toggle. If False, all printing is suppressed
+  *  -  print_training
+     -  True
+     -  [None]
+     -  ['bool']
+     -  Whether to print training information
+  *  -  print_prediction
+     -  True
+     -  [None]
+     -  ['bool']
+     -  Whether to print prediction information
+  *  -  print_problem
+     -  True
+     -  [None]
+     -  ['bool']
+     -  Whether to print problem information
+  *  -  print_solver
+     -  True
+     -  [None]
+     -  ['bool']
+     -  Whether to print solver information
+  *  -  p
+     -  2.5
+     -  [None]
+     -  ['int', 'float']
+     -  order of distance norm
+  *  -  data_dir
+     -  None
+     -  [None]
+     -  ['str']
+     -  Directory for loading / saving cached data; None means do not save or load

doc2/_src_docs/methods/idw.rstx

@@ -0,0 +1,6 @@
+Inverse-distance weighting
+==========================
+
+.. embed-test-plot-print :: _src_docs/methods/test_simple.py , Test , test_idw
+
+.. embed-options-table :: IDW

doc2/_src_docs/methods/krg.rst

@@ -0,0 +1,138 @@
+Kriging
+=======
+
+.. code-block:: python
+
+  import numpy as np
+  import matplotlib.pyplot as plt
+  
+  from smt.methods import KRG
+  
+  xt = np.array([0., 1., 2., 3., 4.])
+  yt = np.array([0., 1., 1.5, 0.5, 1.0])
+  
+  sm = KRG(theta0=[1e-2])
+  sm.set_training_values(xt, yt)
+  sm.train()
+  
+  num = 100
+  x = np.linspace(0., 4., num)
+  y = sm.predict_values(x)
+  
+  plt.plot(xt, yt, 'o')
+  plt.plot(x, y)
+  plt.xlabel('x')
+  plt.ylabel('x')
+  plt.legend(['Training data', 'Prediction'])
+  plt.show()
+  
+.. plot::
+
+  import numpy as np
+  import matplotlib.pyplot as plt
+
+  from smt.methods import KRG
+
+  xt = np.array([0., 1., 2., 3., 4.])
+  yt = np.array([0., 1., 1.5, 0.5, 1.0])
+
+  sm = KRG(theta0=[1e-2])
+  sm.set_training_values(xt, yt)
+  sm.train()
+
+  num = 100
+  x = np.linspace(0., 4., num)
+  y = sm.predict_values(x)
+
+  plt.plot(xt, yt, 'o')
+  plt.plot(x, y)
+  plt.xlabel('x')
+  plt.ylabel('x')
+  plt.legend(['Training data', 'Prediction'])
+  plt.show()
+
+::
+
+  ___________________________________________________________________________
+     
+                                    Kriging
+  ___________________________________________________________________________
+     
+   Problem size
+     
+        # training points.        : 5
+     
+  ___________________________________________________________________________
+     
+   Training
+     
+     Training ...
+     Training - done. Time (sec):  0.0056038
+  ___________________________________________________________________________
+     
+   Evaluation
+     
+        # eval points. : 100
+     
+     Predicting ...
+     Predicting - done. Time (sec):  0.0001500
+     
+     Prediction time/pt. (sec) :  0.0000015
+     
+  
+
+.. list-table:: List of options
+  :header-rows: 1
+  :widths: 15, 10, 20, 20, 30
+  :stub-columns: 0
+
+  *  -  Option
+     -  Default
+     -  Acceptable values
+     -  Acceptable values
+     -  Description
+  *  -  print_global
+     -  True
+     -  [None]
+     -  ['bool']
+     -  Global print toggle. If False, all printing is suppressed
+  *  -  print_training
+     -  True
+     -  [None]
+     -  ['bool']
+     -  Whether to print training information
+  *  -  print_prediction
+     -  True
+     -  [None]
+     -  ['bool']
+     -  Whether to print prediction information
+  *  -  print_problem
+     -  True
+     -  [None]
+     -  ['bool']
+     -  Whether to print problem information
+  *  -  print_solver
+     -  True
+     -  [None]
+     -  ['bool']
+     -  Whether to print solver information
+  *  -  theta0
+     -  None
+     -  [None]
+     -  ['list', 'ndarray']
+     -  Initial hyperparameters
+  *  -  poly
+     -  constant
+     -  ['constant', 'linear', 'quadratic']
+     -  ['function']
+     -  regr. term
+  *  -  corr
+     -  squar_exp
+     -  ['abs_exp', 'squar_exp']
+     -  ['function']
+     -  type of corr. func.
+  *  -  data_dir
+     -  None
+     -  [None]
+     -  ['str']
+     -  Directory for loading / saving cached data; None means do not save or load

doc2/_src_docs/methods/krg.rstx

@@ -0,0 +1,6 @@
+Kriging
+=======
+
+.. embed-test-plot-print :: _src_docs/methods/test_simple.py , Test , test_krg
+
+.. embed-options-table :: KRG