diff options
| author | Calvin <calvin@EESI> | 2013-03-26 13:44:35 -0400 |
|---|---|---|
| committer | Calvin <calvin@EESI> | 2013-03-26 13:44:35 -0400 |
| commit | eac04614526d7d619822bbabf46543053e5e7200 (patch) | |
| tree | 5e4be62de68126cb9569b03ece6666ff391ea417 /python | |
| parent | 94da049a53423a9aca04d8376b75347ca7eadcbe (diff) | |
renamed a bunch of stuff
Diffstat (limited to 'python')
| -rw-r--r-- | python/LICENSE.txt | 165 | ||||
| -rw-r--r-- | python/create_digits_data.py | 16 | ||||
| -rw-r--r-- | python/demo_feast_wrapper.py | 114 | ||||
| -rw-r--r-- | python/digit.txt | 1797 | ||||
| -rw-r--r-- | python/feast.py | 412 | ||||
| -rw-r--r-- | python/import_data.py | 58 |
6 files changed, 0 insertions, 2562 deletions
diff --git a/python/LICENSE.txt b/python/LICENSE.txt deleted file mode 100644 index 65c5ca8..0000000 --- a/python/LICENSE.txt +++ /dev/null @@ -1,165 +0,0 @@ - GNU LESSER GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/> - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - - This version of the GNU Lesser General Public License incorporates -the terms and conditions of version 3 of the GNU General Public -License, supplemented by the additional permissions listed below. - - 0. Additional Definitions. - - As used herein, "this License" refers to version 3 of the GNU Lesser -General Public License, and the "GNU GPL" refers to version 3 of the GNU -General Public License. - - "The Library" refers to a covered work governed by this License, -other than an Application or a Combined Work as defined below. - - An "Application" is any work that makes use of an interface provided -by the Library, but which is not otherwise based on the Library. -Defining a subclass of a class defined by the Library is deemed a mode -of using an interface provided by the Library. - - A "Combined Work" is a work produced by combining or linking an -Application with the Library. The particular version of the Library -with which the Combined Work was made is also called the "Linked -Version". - - The "Minimal Corresponding Source" for a Combined Work means the -Corresponding Source for the Combined Work, excluding any source code -for portions of the Combined Work that, considered in isolation, are -based on the Application, and not on the Linked Version. - - The "Corresponding Application Code" for a Combined Work means the -object code and/or source code for the Application, including any data -and utility programs needed for reproducing the Combined Work from the -Application, but excluding the System Libraries of the Combined Work. - - 1. Exception to Section 3 of the GNU GPL. - - You may convey a covered work under sections 3 and 4 of this License -without being bound by section 3 of the GNU GPL. - - 2. Conveying Modified Versions. - - If you modify a copy of the Library, and, in your modifications, a -facility refers to a function or data to be supplied by an Application -that uses the facility (other than as an argument passed when the -facility is invoked), then you may convey a copy of the modified -version: - - a) under this License, provided that you make a good faith effort to - ensure that, in the event an Application does not supply the - function or data, the facility still operates, and performs - whatever part of its purpose remains meaningful, or - - b) under the GNU GPL, with none of the additional permissions of - this License applicable to that copy. - - 3. Object Code Incorporating Material from Library Header Files. - - The object code form of an Application may incorporate material from -a header file that is part of the Library. You may convey such object -code under terms of your choice, provided that, if the incorporated -material is not limited to numerical parameters, data structure -layouts and accessors, or small macros, inline functions and templates -(ten or fewer lines in length), you do both of the following: - - a) Give prominent notice with each copy of the object code that the - Library is used in it and that the Library and its use are - covered by this License. - - b) Accompany the object code with a copy of the GNU GPL and this license - document. - - 4. Combined Works. - - You may convey a Combined Work under terms of your choice that, -taken together, effectively do not restrict modification of the -portions of the Library contained in the Combined Work and reverse -engineering for debugging such modifications, if you also do each of -the following: - - a) Give prominent notice with each copy of the Combined Work that - the Library is used in it and that the Library and its use are - covered by this License. - - b) Accompany the Combined Work with a copy of the GNU GPL and this license - document. - - c) For a Combined Work that displays copyright notices during - execution, include the copyright notice for the Library among - these notices, as well as a reference directing the user to the - copies of the GNU GPL and this license document. - - d) Do one of the following: - - 0) Convey the Minimal Corresponding Source under the terms of this - License, and the Corresponding Application Code in a form - suitable for, and under terms that permit, the user to - recombine or relink the Application with a modified version of - the Linked Version to produce a modified Combined Work, in the - manner specified by section 6 of the GNU GPL for conveying - Corresponding Source. - - 1) Use a suitable shared library mechanism for linking with the - Library. A suitable mechanism is one that (a) uses at run time - a copy of the Library already present on the user's computer - system, and (b) will operate properly with a modified version - of the Library that is interface-compatible with the Linked - Version. - - e) Provide Installation Information, but only if you would otherwise - be required to provide such information under section 6 of the - GNU GPL, and only to the extent that such information is - necessary to install and execute a modified version of the - Combined Work produced by recombining or relinking the - Application with a modified version of the Linked Version. (If - you use option 4d0, the Installation Information must accompany - the Minimal Corresponding Source and Corresponding Application - Code. If you use option 4d1, you must provide the Installation - Information in the manner specified by section 6 of the GNU GPL - for conveying Corresponding Source.) - - 5. Combined Libraries. - - You may place library facilities that are a work based on the -Library side by side in a single library together with other library -facilities that are not Applications and are not covered by this -License, and convey such a combined library under terms of your -choice, if you do both of the following: - - a) Accompany the combined library with a copy of the same work based - on the Library, uncombined with any other library facilities, - conveyed under the terms of this License. - - b) Give prominent notice with the combined library that part of it - is a work based on the Library, and explaining where to find the - accompanying uncombined form of the same work. - - 6. Revised Versions of the GNU Lesser General Public License. - - The Free Software Foundation may publish revised and/or new versions -of the GNU Lesser General Public License from time to time. Such new -versions will be similar in spirit to the present version, but may -differ in detail to address new problems or concerns. - - Each version is given a distinguishing version number. If the -Library as you received it specifies that a certain numbered version -of the GNU Lesser General Public License "or any later version" -applies to it, you have the option of following the terms and -conditions either of that published version or of any later version -published by the Free Software Foundation. If the Library as you -received it does not specify a version number of the GNU Lesser -General Public License, you may choose any version of the GNU Lesser -General Public License ever published by the Free Software Foundation. - - If the Library as you received it specifies that a proxy can decide -whether future versions of the GNU Lesser General Public License shall -apply, that proxy's public statement of acceptance of any version is -permanent authorization for you to choose that version for the -Library. diff --git a/python/create_digits_data.py b/python/create_digits_data.py deleted file mode 100644 index 891eea8..0000000 --- a/python/create_digits_data.py +++ /dev/null @@ -1,16 +0,0 @@ -#!/usr/bin/env python -from sklearn import datasets - -digits = datasets.load_digits() # load the data from scikits -data = digits.images.reshape((digits.images.shape[0], -1)) -labels = digits.target # extract the labels - -fw = open('digit.txt', 'w') - -for n in range(len(data)): - mstr = '' - for x in data[n]: - mstr += str(x) + '\t' - fw.write(mstr + str(labels[n]) + '\n') - -fw.close()
\ No newline at end of file diff --git a/python/demo_feast_wrapper.py b/python/demo_feast_wrapper.py deleted file mode 100644 index 0a9633a..0000000 --- a/python/demo_feast_wrapper.py +++ /dev/null @@ -1,114 +0,0 @@ -#!/usr/bin/env python -from feast import * -import numpy as np -import import_data - - -def check_result(selected_features, n_relevant): - selected_features = sorted(selected_features) - success = True - for k in range(n_relevant): - if k != selected_features[k]: - success = False - return success - - - -n_relevant = 5 -data_source = 'uniform' # set the data set we want to test - - -if data_source == 'uniform': - data, labels = import_data.uniform_data(n_relevant = n_relevant) -elif data_source == 'digits': - data, labels = import_data.read_digits('digit.txt') - -n_observations = len(data) # number of samples in the data set -n_features = len(data.transpose()) # number of features in the data set -n_select = 15 # how many features to select -method = 'JMI' # feature selection algorithm - - -print '---> Information' -print ' :n_observations - ' + str(n_observations) -print ' :n_features - ' + str(n_features) -print ' :n_select - ' + str(n_select) -print ' :algorithm - ' + str(method) -print ' ' -print '---> Running unit tests on FEAST 4 Python... ' - - -################################################################# -################################################################# -print ' Running BetaGamma... ' -sf = BetaGamma(data, labels, n_select, beta=0.5, gamma=0.5) -if check_result(sf, n_relevant) == True: - print ' BetaGamma passed!' -else: - print ' BetaGamma failed!' - - -################################################################# -################################################################# -print ' Running CMIM... ' -sf = CMIM(data, labels, n_select) -if check_result(sf, n_relevant) == True: - print ' CMIM passed!' -else: - print ' CMIM failed!' - - -################################################################# -################################################################# -print ' Running CondMI... ' -sf = CondMI(data, labels, n_select) -if check_result(sf, n_relevant) == True: - print ' CondMI passed!' -else: - print ' CondMI failed!' - - -################################################################# -################################################################# -print ' Running DISR... ' -sf = DISR(data, labels, n_select) -if check_result(sf, n_relevant) == True: - print ' DISR passed!' -else: - print ' DISR failed!' - - -################################################################# -################################################################# -print ' Running ICAP... ' -sf = ICAP(data, labels, n_select) -if check_result(sf, n_relevant) == True: - print ' ICAP passed!' -else: - print ' ICAP failed!' - - -################################################################# -################################################################# -print ' Running JMI... ' -sf = JMI(data, labels, n_select) -if check_result(sf, n_relevant) == True: - print ' JMI passed!' -else: - print ' JMI failed!' - - -################################################################# -################################################################# -print ' Running mRMR... ' -sf = mRMR(data, labels, n_select) -if check_result(sf, n_relevant) == True: - print ' mRMR passed!' -else: - print ' mRMR failed!' - -print '---> Done unit tests!' - - - - diff --git a/python/digit.txt b/python/digit.txt deleted file mode 100644 index ed966df..0000000 --- a/python/digit.txt +++ /dev/null @@ -1,1797 +0,0 @@ -0.0 0.0 5.0 13.0 9.0 1.0 0.0 0.0 0.0 0.0 13.0 15.0 10.0 15.0 5.0 0.0 0.0 3.0 15.0 2.0 0.0 11.0 8.0 0.0 0.0 4.0 12.0 0.0 0.0 8.0 8.0 0.0 0.0 5.0 8.0 0.0 0.0 9.0 8.0 0.0 0.0 4.0 11.0 0.0 1.0 12.0 7.0 0.0 0.0 2.0 14.0 5.0 10.0 12.0 0.0 0.0 0.0 0.0 6.0 13.0 10.0 0.0 0.0 0.0 0 -0.0 0.0 0.0 12.0 13.0 5.0 0.0 0.0 0.0 0.0 0.0 11.0 16.0 9.0 0.0 0.0 0.0 0.0 3.0 15.0 16.0 6.0 0.0 0.0 0.0 7.0 15.0 16.0 16.0 2.0 0.0 0.0 0.0 0.0 1.0 16.0 16.0 3.0 0.0 0.0 0.0 0.0 1.0 16.0 16.0 6.0 0.0 0.0 0.0 0.0 1.0 16.0 16.0 6.0 0.0 0.0 0.0 0.0 0.0 11.0 16.0 10.0 0.0 0.0 1 -0.0 0.0 0.0 4.0 15.0 12.0 0.0 0.0 0.0 0.0 3.0 16.0 15.0 14.0 0.0 0.0 0.0 0.0 8.0 13.0 8.0 16.0 0.0 0.0 0.0 0.0 1.0 6.0 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15.0 8.0 15.0 0.0 0.0 0.0 0.0 5.0 16.0 16.0 10.0 0.0 0.0 0.0 0.0 12.0 15.0 15.0 12.0 0.0 0.0 0.0 4.0 16.0 6.0 4.0 16.0 6.0 0.0 0.0 8.0 16.0 10.0 8.0 16.0 8.0 0.0 0.0 1.0 8.0 12.0 14.0 12.0 1.0 0.0 8 diff --git a/python/feast.py b/python/feast.py deleted file mode 100644 index 0d2fee6..0000000 --- a/python/feast.py +++ /dev/null @@ -1,412 +0,0 @@ -import numpy as np -from ctypes import * - - -''' - The FEAST module provides an interface between the C-library - for feature selection to Python. - - References: - 1) G. Brown, A. Pocock, M.-J. Zhao, and M. Lujan, "Conditional - likelihood maximization: A unifying framework for information - theoretic feature selection," Journal of Machine Learning - Research, vol. 13, pp. 27-66, 2012. - - - __author__ = "Calvin Morrison" - __copyright__ = "Copyright 2013, EESI Laboratory" - __credits__ = ["Calvin Morrison", "Gregory Ditzler"] - __license__ = "GPL" - __version__ = "0.1.0" - __maintainer__ = "Calvin Morrison" - __email__ = "mutantturkey@gmail.com" - __status__ = "Release" -''' - -# I listed the function definitions in alphabetical order. Lets -# keep this up. - - -try: - libFSToolbox = CDLL("libFSToolbox.so"); -except: - print "Error: could not find libFSToolbox" - exit() - - -def BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0): - ''' - BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0) - - This algotihm implements conditional mutual information - feature select, such that beta and gamma control the - weight attached to the redundant mutual and conditional - mutual information, respectively. - - Input - :data - data in a Numpy array such that len(data) = - n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with - n_observations as the number of elements. That is - len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - :beta - penalty attacted to I(X_j;X_k) - :gamma - positive weight attached to the conditional - redundancy term I(X_k;X_j|Y) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' - # python values - n_observations, n_features = data.shape - output = np.zeros(n_select) - - # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) - c_beta = c_double(beta) - c_gamma = c_double(gamma) - - libFSToolbox.BetaGamma.restype = POINTER(c_double * n_select) - features = libFSToolbox.BetaGamma(c_n_select, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)), - c_beta, - c_gamma - ) - - # turn our output into a list - selected_features = [] - for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) - - return selected_features - - - -def CMIM(data, labels, n_select): - ''' - CMIM(data, labels, n_select) - - This function implements the conditional mutual information - maximization feature selection algorithm. Note that this - implementation does not allow for the weighting of the - redundancy terms that BetaGamma will allow you to do. - - Input - :data - data in a Numpy array such that len(data) = - n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with - n_observations as the number of elements. That is - len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' - - # python values - n_observations, n_features = data.shape - output = np.zeros(n_select) - - # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) - - libFSToolbox.CMIM.restype = POINTER(c_double * n_select) - features = libFSToolbox.CMIM(c_n_select, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)) - ) - - - # turn our output into a list - selected_features = [] - for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) - - return selected_features - - - -def CondMI(data, labels, n_select): - ''' - CondMI(data, labels, n_select) - - This function implements the conditional mutual information - maximization feature selection algorithm. - - Input - :data - data in a Numpy array such that len(data) = - n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with - n_observations as the number of elements. That is - len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' - # python values - n_observations, n_features = data.shape - output = np.zeros(n_select) - - # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) - - libFSToolbox.CondMI.restype = POINTER(c_double * n_select) - features = libFSToolbox.CondMI(c_n_select, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)) - ) - - - # turn our output into a list - selected_features = [] - for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) - - return selected_features - - - - - - -def DISR(data, labels, n_select): - ''' - DISR(data, labels, n_select) - - This function implements the double input symmetrical relevance - feature selection algorithm. - - Input - :data - data in a Numpy array such that len(data) = - n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with - n_observations as the number of elements. That is - len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' - # python values - n_observations, n_features = data.shape - output = np.zeros(n_select) - - # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) - - libFSToolbox.DISR.restype = POINTER(c_double * n_select) - features = libFSToolbox.DISR(c_n_select, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)) - ) - - - # turn our output into a list - selected_features = [] - for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) - - return selected_features - - - - -def ICAP(data, labels, n_select): - ''' - ICAP(data, labels, n_select) - - This function implements the interaction capping feature - selection algorithm. - - Input - :data - data in a Numpy array such that len(data) = - n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with - n_observations as the number of elements. That is - len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' - # python values - n_observations, n_features = data.shape - output = np.zeros(n_select) - - # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) - - libFSToolbox.ICAP.restype = POINTER(c_double * n_select) - features = libFSToolbox.ICAP(c_n_select, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)) - ) - - - # turn our output into a list - selected_features = [] - for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) - - return selected_features - - - - - -def JMI(data, labels, n_select): - ''' - JMI(data, labels, n_select) - - This function implements the joint mutual information feature - selection algorithm. - - Input - :data - data in a Numpy array such that len(data) = - n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with - n_observations as the number of elements. That is - len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' - - # python values - n_observations, n_features = data.shape - output = np.zeros(n_select) - - # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) - - libFSToolbox.JMI.restype = POINTER(c_double * n_select) - features = libFSToolbox.JMI(c_n_select, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)) - ) - - - # turn our output into a list - selected_features = [] - for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) - - return selected_features - -def mRMR(data, labels, n_select): - ''' - mRMR(data, labels, n_select) - - This funciton implements the max-relevance min-redundancy feature - selection algorithm. - - Input - :data - data in a Numpy array such that len(data) = - n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with - n_observations as the number of elements. That is - len(labels) = len(data) = n_observations. - (REQUIRED) - :n_select - number of features to select. (REQUIRED) - Output - :selected_features - returns a list containing the features - in the order they were selected. - ''' - - # python values - n_observations, n_features = data.shape - output = np.zeros(n_select) - - # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) - - libFSToolbox.mRMR_D.restype = POINTER(c_double * n_select) - features = libFSToolbox.mRMR_D(c_n_select, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)) - ) - - - # turn our output into a list - selected_features = [] - for i in features.contents: - # recall that feast was implemented with Matlab in mind, so the - # authors assumed the indexing started a one; however, in Python - # the indexing starts at zero. - selected_features.append(i - 1) - - return selected_features - - - - - diff --git a/python/import_data.py b/python/import_data.py deleted file mode 100644 index 6d4bd9e..0000000 --- a/python/import_data.py +++ /dev/null @@ -1,58 +0,0 @@ - - - - -################################################################## -################################################################## -################################################################## -def read_digits(fname='digit.txt'): - ''' - read_digits(fname='digit.txt') - - read a data file that contains the features and class labels. - each row of the file is a feature vector with the class - label appended. - ''' - import csv - import numpy as np - - fw = csv.reader(open(fname,'rb'), delimiter='\t') - data = [] - for line in fw: - data.append( [float(x) for x in line] ) - data = np.array(data) - labels = data[:,len(data.transpose())-1] - data = data[:,:len(data.transpose())-1] - return data, labels -################################################################## -################################################################## -################################################################## - - - -################################################################## -################################################################## -################################################################## -def uniform_data(n_observations = 1000, n_features = 50, n_relevant = 5): - import numpy as np - xmax = 10 - xmin = 0 - data = np.random.randint(xmax + 1, size = (n_features, n_observations)) - labels = np.zeros(n_observations) - delta = n_relevant * (xmax - xmin) / 2.0 - - for m in range(n_observations): - zz = 0.0 - for k in range(n_relevant): - zz += data[k, m] - if zz > delta: - labels[m] = 1 - else: - labels[m] = 2 - data = data.transpose() - - return data, labels - -################################################################## -################################################################## -################################################################## |