initialize gwr submodule

.travis.yml

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+language: python
+sudo: false
+branches:
+only:
+  - master
+python:
+  - "2.7"
+  - "3.5"
+  - "3.6"
+
+env:
+  - PYSAL_PLUS=false
+  - PYSAL_PLUS=true
+
+before_install:
+  - wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
+  - chmod +x miniconda.sh
+  - ./miniconda.sh -b -p ./miniconda
+  - export PATH=`pwd`/miniconda/bin:$PATH
+  - conda update --yes conda
+  - conda config --add channels conda-forge
+  - conda create -y -q -n test-env python=$TRAVIS_PYTHON_VERSION
+  - source activate test-env
+  - if [[ $TRAVIS_PYTHON_VERSION == 3* ]]; 
+        then 2to3 -nw gwr > /dev/null; 
+    fi
+
+install:
+  - conda install --yes pip
+  - conda install --yes --file requirements.txt;
+  - conda install --yes nose
+  - pip install libpysal
+  - cd ../ && git clone https://github.com/TaylorOshan/spglm.git
+  - if [[ $TRAVIS_PYTHON_VERSION == 3* ]]; 
+        then 2to3 -nw spglm/ > /dev/null; 
+    fi
+  - pip install -e ./spglm
+  - cd ./gwr
+  - pip install -r requirements_dev.txt
+
+script:
+  - python setup.py sdist >/dev/null
+  - nosetests --with-coverage --cover-package=gwr;
+
+notifications:
+    email:
+        recipients:
+            - [email protected]
+        on_change: always
+        on_failure: always
+
+after_success:
+  - coveralls

LICENSE

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+BSD 2-Clause License
+
+Copyright (c) 2017, pysal
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

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+**G**eographically **W**eighted **R**egression
+=======================================
+
+Description goes here
+
+Features
+--------
+
+- Feature 1
+- Feature 2
+- Feature 3
+
+Future Work
+-----------
+
+- Future work 1
+- Future work 2
+
+
+

gwr/__init__.py

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+from . import gwr
+from . import sel_bw
+from . import diagnostics
+from . import kernels

gwr/diagnostics.py

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+"""
+Diagnostics for estimated gwr models
+"""
+__author__ = "Taylor Oshan [email protected]"
+
+import numpy as np
+from spglm.family import Gaussian, Poisson, Binomial
+
+def get_AICc(gwr):
+    """
+    Get AICc value
+    
+    Gaussian: p61, (2.33), Fotheringham, Brunsdon and Charlton (2002)
+    
+    GWGLM: AICc=AIC+2k(k+1)/(n-k-1), Nakaya et al. (2005): p2704, (36)
+
+    """
+    n = gwr.n
+    k = gwr.tr_S
+    if isinstance(gwr.family, Gaussian):
+        aicc = -2.0*gwr.llf + 2.0*n*(k + 1.0)/(n-k-2.0)  
+    elif isinstance(gwr.family, (Poisson, Binomial)):
+        aicc = get_AIC(gwr) + 2.0 * k * (k+1.0) / (n - k - 1.0) 
+    return aicc
+
+def get_AIC(gwr):
+    """
+    Get AIC calue
+
+    Gaussian: p96, (4.22), Fotheringham, Brunsdon and Charlton (2002)
+
+    GWGLM:  AIC(G)=D(G) + 2K(G), where D and K denote the deviance and the effective
+    number of parameters in the model with bandwidth G, respectively.
+    
+    """   
+    k = gwr.tr_S
+    #deviance = -2*log-likelihood
+    y = gwr.y
+    mu = gwr.mu
+    if isinstance(gwr.family, Gaussian):
+        aic = -2.0 * gwr.llf + 2.0 * (k+1)
+    elif isinstance(gwr.family, (Poisson, Binomial)):
+        aic = np.sum(gwr.family.resid_dev(y, mu)**2) + 2.0 * k
+    return aic 
+
+def get_BIC(gwr):
+    """
+    Get BIC value
+
+    Gaussian: p61 (2.34), Fotheringham, Brunsdon and Charlton (2002)
+    BIC = -2log(L)+klog(n)
+
+    GWGLM: BIC = dev + tr_S * log(n)
+
+    """
+    n = gwr.n      # (scalar) number of observations
+    k = gwr.tr_S  
+    y = gwr.y
+    mu = gwr.mu
+    if isinstance(gwr.family, Gaussian):
+        bic = -2.0 * gwr.llf + (k+1) * np.log(n) 
+    elif isinstance(gwr.family, (Poisson, Binomial)):
+        bic = np.sum(gwr.family.resid_dev(y, mu)**2) + k * np.log(n)
+    return bic
+
+def get_CV(gwr):
+    """
+    Get CV value
+
+    Gaussian only
+
+    Methods: p60, (2.31) or p212 (9.4)
+    Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+    Geographically weighted regression: the analysis of spatially varying relationships.
+    Modification: sum of residual squared is divided by n according to GWR4 results
+
+    """
+    aa = gwr.resid_response.reshape((-1,1))/(1.0-gwr.influ)
+    cv = np.sum(aa**2)/gwr.n
+    return cv
+