Version 0.1

7 files changed, 711 insertions, 6 deletions

diff --git a/README.md b/README.md
index 8c87722..6c22793 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,37 @@
-# multired
-Algorithm for structural reduction of multi-layer networks
+#multired
+*
+* Copyright (C) 2015 Vincenzo (Enzo) Nicosia <katolaz@yahoo.it>
+*
+* This program is free software: you can redistribute it and/or modify
+* it under the terms of the GNU General Public License as published by
+* the Free Software Foundation, either version 3 of the License, or
+* (at your option) any later version.  
+*
+* This program is distributed in the hope that it will be useful, but
+* WITHOUT ANY WARRANTY; without even the implied warranty of
+* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+* General Public License for more details.
+*
+* You should have received a copy of the GNU General Public License
+* long with this program.  If not, see  <http://www.gnu.org/licenses/>.
+*
+ 
+----------------------
+This is multired-0.1.
+
+
+This is a Python implmementation of the algorithm for structural
+reduction of multi-layer networks based on the Von Neumann and on the
+Quantum Jensen-Shannon divergence of graphs, as explained in:
+
+  M. De Domenico. V. Nicosia, A. Arenas, V. Latora
+  "Structural reducibility of multilayer networks", 
+  Nat. Commun. 6, 6864 (2015) doi:10.1038/ncomms7864
+
+If you happen to find any use of this code please do not forget to
+cite that paper ;-)
+
+My plan is to provide also a C version in the future, but I cannot 
+guarantee that I will do so anytime soon. 
 
-Please cite:
 
-M. De Domenico. V. Nicosia, A. Arenas, V. Latora
-"Structural reducibility of multilayer networks", 
-Nat. Commun. 7864 (2015) doi:10.1038/ncomms7864
diff --git a/python/README.md b/python/README.md
new file mode 100644
index 0000000..b63b36c
--- /dev/null
+++ b/python/README.md
@@ -0,0 +1,83 @@
+# multired
+/**
+*
+* Copyright (C) 2015 Vincenzo (Enzo) Nicosia <katolaz@yahoo.it>
+*
+*
+* This program is free software: you can redistribute it and/or modify
+* it under the terms of the GNU General Public License as published by
+* the Free Software Foundation, either version 3 of the License, or
+* (at your option) any later version.  
+*
+* This program is distributed in the hope that it will be useful, but
+* WITHOUT ANY WARRANTY; without even the implied warranty of
+* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+* General Public License for more details.
+*
+* You should have received a copy of the GNU General Public License
+* long with this program.  If not, see  <http://www.gnu.org/licenses/>.
+*
+*/
+
+This is multired-0.1.
+
+
+This is a Python implmementation of the algorithm for structural
+reduction of multi-layer networks based on the Von Neumann and on the
+Quantum Jensen-Shannon divergence of graphs, as explained in:
+
+  M. De Domenico. V. Nicosia, A. Arenas, V. Latora
+  "Structural reducibility of multilayer networks", 
+  Nat. Commun. 6, 6864 (2015) doi:10.1038/ncomms7864
+
+If you happen to find any use of this code please do not forget to
+cite that paper ;-)
+
+
+--------------------
+       INFO
+--------------------
+
+The module "multired.py" provides the class "multiplex_red", which
+implements the algorithm to reduce a multilayer network described in
+the paper cited above. 
+
+In order to use it, you just need to 
+
+ import multired as mr
+
+in your python script and create a multiplex_red object. Please make
+sure that "multired.py" is in PYTHONPATH. The constructor requires as
+its first argument the path of a file which in turn contains a list of
+files (one for each line) where the graph of each layer is to be
+found.
+
+The class provides one set of methods which perform the exact
+evaluation of the Von Neumann entropy, and another set of methods
+(those whose name end with the suffix "_approx") which rely on a
+polynomial approximation of the Von Neumann entropy. By default the
+approximation is based on a 10th order polynomial fit of x log(x) in
+[0,1], but the order of the polynomial can be set through the
+parameter "fit_degree" of the constructor.
+
+Several sample scripts can be found in the "test/" directory. 
+
+--------------------
+    DEPENDENCIES
+--------------------
+
+The only strict dependencies are a recent version of Python, Numpy and
+SciPy. The methods "draw_dendrogram" and "draw_dendrogram_approx" will
+work only if matplotlib is installed.
+
+The module has been tested on a Debian GNU/Linux system, using:
+
+ - Python 2.7.8,  
+ - SciPy 0.13.3
+ - Numpy 1.8.2
+ - matplotlib 1.3.1
+
+but it will almost surely work on other platforms and/or with other
+versions of those packages. If you would like to report a working
+configuration, just email me (the address is at the beginning of this
+file).
diff --git a/python/multired.py b/python/multired.py
new file mode 100644
index 0000000..7482877
--- /dev/null
+++ b/python/multired.py
@@ -0,0 +1,487 @@
+import sys
+import math 
+import numpy as np
+from scipy.sparse import csr_matrix, eye
+from scipy.linalg import eigh, eig
+import copy
+from scipy.cluster.hierarchy import linkage, dendrogram
+
+has_matplotlib = False
+
+try:
+    import matplotlib
+    has_matplotlib = True
+
+except ImportError:
+    has_matplotlib = False
+
+
+class XLogx_fit:
+    def __init__(self, degree, npoints= 100, xmax=1):
+        if xmax > 1:
+            xmax = 1
+        self.degree = degree
+        x = np.linspace(0, xmax, npoints)
+        y = [i * math.log(i) for i in x[1:]]
+        y.insert(0, 0)
+        self.fit = np.polyfit(x, y, degree)
+
+    def __getitem__ (self, index):
+        if index <= self.degree:
+            return self.fit[index]
+        else:
+            print "Error!!! Index %d is larger than the degree of the fitting polynomial (%d)" \
+                % (index, degree)
+            sys.exit(-1)
+
+
+class layer:
+    def __init__ (self, layerfile= None, matrix=None):
+        self.N = 0
+        self.num_layer = -1
+        self.fname = layerfile
+        self.adj_matr = None
+        self.laplacian = None
+        self.resc_laplacian = None
+        self.entropy = None
+        self.entropy_approx = None
+        self._ii = []
+        self._jj = []
+        self._ww = []
+        self._matrix_called = False
+        if layerfile != None:
+            try:
+                min_N = 10e10
+                with open(layerfile, "r") as lines:
+                    for l in lines:
+                        if l[0] == '#':
+                            continue
+                        elems = l.strip(" \n").split(" ")
+                        s = int(elems[0])
+                        d = int(elems[1])
+                        self._ii.append(s)
+                        self._jj.append(d)
+                        if s > self.N:
+                            self.N = s
+                        if d > self.N:
+                            self.N = d
+                        if s < min_N:
+                            min_N = s
+                        if  d < min_N:
+                            min_N = d
+                        if len(elems) >2 : ## A weight is specified 
+                            val = [float(x) if "e" in x or "." in x else int(x) for x in [elems[2]]][0]
+                            self._ww.append(float(val))
+                        else:
+                            self._ww.append(int(1))
+                    
+            except (IOError):
+                print "Unable to find/open file %s -- Exiting!!!" % layerfile
+                exit(-2)
+        elif matrix != None:
+            self.adj_matr = copy.copy(matrix)
+            self.N, _x = matrix.shape 
+            K = np.multiply(self.adj_matr.sum(0), np.ones((self.N,self.N)))
+            D = np.diag(np.diag(K))
+            self.laplacian = csr_matrix(D - self.adj_matr)
+            K = self.laplacian.diagonal().sum()
+            self.resc_laplacian = csr_matrix(self.laplacian / K)
+            self._matrix_called = True
+        else:
+            print "The given matrix is BLANK"
+    def make_matrices(self, N):
+        self.N = N 
+        self.adj_matr = csr_matrix((self._ww, (self._ii, self._jj)), shape=(self.N, self.N))
+        self.adj_matr = self.adj_matr + self.adj_matr.transpose()
+        K = np.multiply(self.adj_matr.sum(0), np.ones((self.N,self.N)))
+        D = np.diag(np.diag(K))
+        self.laplacian = csr_matrix(D - self.adj_matr)
+        K = self.laplacian.diagonal().sum()
+        self.resc_laplacian = csr_matrix(self.laplacian / K)
+        self._matrix_called = True
+    
+    def dump_info(self):
+        N, M = self.adj_matr.shape
+        K = self.adj_matr.nnz
+        sys.stderr.write("Layer File: %s\nNodes: %d Edges: %d\nEntropy: %g Approx. Entropy: %g\n" % \
+                             (self.fname, N, K, self.entropy, self.entropy_approx) )
+
+    def compute_VN_entropy(self):
+        eigvals = eigh(self.resc_laplacian.todense())
+
+        self.entropy = 0
+        for l_i in eigvals[0]:
+            if (l_i > 10e-20):
+                self.entropy -= l_i * math.log (l_i)
+
+
+    def compute_VN_entropy_approx(self, poly):
+        p = poly.degree
+        h = - poly[p] * self.N
+        M = csr_matrix(np.eye(self.N))
+        for i in range(p-1, -1, -1):
+            M = M *  self.resc_laplacian
+            h += - poly[i] * sum(M.diagonal())
+        self.entropy_approx = h
+
+    def aggregate(self, other_layer):
+        if self.adj_matr != None:
+            self.adj_matr = self.adj_matr + other_layer.adj_matr
+        else:
+            self.adj_matr = copy.copy(other_layer.adj_matr)
+        K = np.multiply(self.adj_matr.sum(0), np.ones((self.N,self.N)))
+        D = np.diag(np.diag(K))
+        self.laplacian = csr_matrix(D - self.adj_matr)
+        K = self.laplacian.diagonal().sum()
+        self.resc_laplacian = csr_matrix(self.laplacian / K)
+        self._matrix_called = True
+
+        
+
+class multiplex_red:
+    
+    def __init__ (self, multiplexfile, directed = None, fit_degree=10, verbose=False):
+        self.layers = []
+        self.N = 0
+        self.M = 0
+        self.entropy = 0
+        self.entropy_approx = 0
+        self.JSD = None
+        self.JSD_approx = None
+        self.Z = None
+        self.Z_approx = None
+        self.aggr = None
+        self.q_vals = None
+        self.q_vals_approx = None
+        self.fit_degree = fit_degree
+        self.poly = XLogx_fit(self.fit_degree)
+        self.verb = verbose
+        self.cuts = None
+        self.cuts_approx = None
+        try:
+            with open(multiplexfile, "r") as lines:
+                for l in lines:
+                    if (self.verb):
+                        sys.stderr.write("Loading layer %d from file %s" % (len(self.layers), l))
+                    A = layer(l.strip(" \n"))
+                    if A.N > self.N:
+                        self.N = A.N+1
+                    self.layers.append(A)
+                    n = 0
+                    for l in self.layers:
+                        l.make_matrices(self.N)
+                        l.num_layer = n
+                        n += 1
+                    self.M = len(self.layers)
+        except ( IOError):
+            print "Unable to find/open file %s -- Exiting!!!" % layer_file
+            exit(-2)
+
+    def dump_info(self):
+        i = 0
+        for l in self.layers:
+            sys.stderr.write("--------\nLayer: %d\n" % i)
+            l.dump_info()
+            i += 1
+
+
+    def compute_aggregated(self):
+        self.aggr = copy.copy(self.layers[0])
+        self.aggr.entropy = 0
+        self.aggr.entropy_approx = 0
+        for l in self.layers[1:]:
+            self.aggr.aggregate(l)
+
+    def compute_layer_entropies(self):
+        for l in self.layers:
+            l.compute_VN_entropy()
+
+    def compute_layer_entropies_approx(self):
+        for l in self.layers:
+            l.compute_VN_entropy_approx(self.poly)
+
+
+    def compute_multiplex_entropy(self, force_compute=False):
+        ### The entropy of a multiplex is defined as the sum of the entropies of its layers
+        for l in self.layers:
+            if l.entropy == None:
+                l.compute_VN_entropy()
+                self.entropy += l.entropy
+
+    def compute_multiplex_entropy_approx(self, force_compute=False):
+        ### The entropy of a multiplex is defined as the sum of the entropies of its layers
+        for l in self.layers:
+            if l.entropy_approx == None:
+                l.compute_VN_entropy_approx(self.poly)
+            self.entropy_approx += l.entropy_approx
+
+    def compute_JSD_matrix(self):
+        if (self.verb):
+            sys.stderr.write("Computing JSD matrix\n")
+        self.JSD = np.zeros((self.M, self.M))
+        for i in range(len(self.layers)):
+            for j in range(i+1, len(self.layers)):
+                li = self.layers[i]
+                lj = self.layers[j]
+                if not li.entropy:
+                    li.compute_VN_entropy()
+                if not lj.entropy:
+                    lj.compute_VN_entropy()
+                # m_sigma = (li.resc_laplacian + lj.resc_laplacian)/2.0
+                # m_sigma_entropy = mr.compute_VN_entropy_LR(m_sigma)
+                m_sigma_matr = (li.adj_matr + lj.adj_matr)/2.0
+                m_sigma = layer(matrix=m_sigma_matr)
+                m_sigma.compute_VN_entropy()
+                d = m_sigma.entropy - 0.5 * (li.entropy + lj.entropy)
+                d = math.sqrt(d)
+                self.JSD[i][j] = d
+                self.JSD[j][i] = d
+        pass
+
+    def compute_JSD_matrix_approx(self):
+        if (self.verb):
+            sys.stderr.write("Computing JSD matrix (approx)\n")
+        self.JSD_approx = np.zeros((self.M, self.M))
+        for i in range(len(self.layers)):
+            for j in range(i+1, len(self.layers)):
+                li = self.layers[i]
+                lj = self.layers[j]
+                if not li.entropy_approx:
+                    li.compute_VN_entropy_approx(self.poly)
+                if not lj.entropy_approx:
+                    lj.compute_VN_entropy_approx(self.poly)
+                m_sigma_matr = (li.adj_matr + lj.adj_matr)/2.0
+                m_sigma = layer(matrix=m_sigma_matr)
+                m_sigma.compute_VN_entropy_approx(self.poly)
+                d = m_sigma.entropy_approx - 0.5 * (li.entropy_approx + lj.entropy_approx)
+                d = math.sqrt(d)
+                self.JSD_approx[i][j] = d
+                self.JSD_approx[j][i] = d
+                
+    def dump_JSD(self, force_compute=False):
+        if self.JSD == None:
+            if force_compute:
+                self.compute_JSD_matrix()
+            else:
+                print "Error!!! call to dump_JSD but JSD matrix has not been computed!!!"
+                sys.exit(1)
+        idx = 0
+        for i in range(self.len):
+            for j in range(i+1, self.len):
+                print i, j, self.JSD[idx]
+                idx += 1
+
+    def dump_JSD_approx(self, force_compute=False):
+        if self.JSD_approx == None:
+            if force_compute:
+                self.compute_JSD_matrix_approx()
+            else:
+                print "Error!!! call to dump_JSD_approx but JSD approximate matrix has not been computed!!!"
+                sys.exit(1)
+        idx = 0
+        for i in range(self.M):
+            for j in range(i+1, self.M):
+                print i, j, self.JSD_approx[idx]
+                idx += 1
+    
+
+    def reduce(self, method="ward"):
+        if (self.verb):
+            sys.stderr.write("Performing '%s' reduction\n" % method)
+        if self.JSD == None:
+            self.compute_JSD_matrix()
+        self.Z = linkage(self.JSD, method=method)
+        return self.Z
+
+    def reduce_approx(self, method="ward"):
+        if (self.verb):
+            sys.stderr.write("Performing '%s' reduction (approx)\n" % method)
+        if self.JSD_approx == None:
+            self.compute_JSD_matrix_approx()
+        self.Z_approx = linkage(self.JSD_approx, method=method)
+        return self.Z_approx
+    
+    def get_linkage(self):
+        return self.Z
+
+    def get_linkage_approx(self):
+        return self.Z_approx
+
+    def __compute_q(self, layers):
+        H_avg = 0
+        if not self.aggr:
+            self.compute_aggregated()
+            self.aggr.compute_VN_entropy()
+        for l in layers:
+            if not l.entropy:
+                l.compute_VN_entropy()
+            H_avg += l.entropy
+        H_avg /= len(layers)
+        q = 1.0 - H_avg / self.aggr.entropy
+        return q
+
+    def get_q_profile(self):
+        mylayers = copy.copy(self.layers)
+        rem_layers = copy.copy(self.layers)
+        q_vals = []
+        if self.Z == None:
+            self.reduce()
+        q = self.__compute_q(rem_layers)
+        q_vals.append(q)
+        n = len(self.layers)
+        for l1, l2, _d, _x in self.Z:
+            l_new = layer(matrix=mylayers[int(l1)].adj_matr)
+            l_new.num_layer = n 
+            n += 1 
+            l_new.aggregate(mylayers[int(l2)])
+            rem_layers.remove(mylayers[int(l1)])
+            rem_layers.remove(mylayers[int(l2)])
+            rem_layers.append(l_new)
+            mylayers.append(l_new)
+            q = self.__compute_q(rem_layers)
+            q_vals.append(q)
+        self.q_vals = q_vals
+        return q_vals
+        pass
+
+    
+    def __compute_q_approx(self, layers):
+        H_avg = 0
+        if not self.aggr:
+            self.compute_aggregated()
+            self.aggr.compute_VN_entropy_approx(self.poly)
+        for l in layers:
+            if not l.entropy_approx:
+                l.compute_VN_entropy_approx(self.poly)
+            H_avg += l.entropy_approx
+        H_avg /= len(layers)
+        q = 1.0 - H_avg / self.aggr.entropy_approx
+        return q
+
+    def get_q_profile_approx(self):
+        mylayers = copy.copy(self.layers)
+        rem_layers = copy.copy(self.layers)
+        q_vals = []
+        if self.Z_approx == None:
+            self.reduce_approx()
+        q = self.__compute_q_approx(rem_layers)
+        q_vals.append(q)
+        n = len(self.layers)
+        for l1, l2, _d, _x in self.Z_approx:
+            l_new = layer(matrix=mylayers[int(l1)].adj_matr)
+            l_new.num_layer = n 
+            n += 1 
+            l_new.aggregate(mylayers[int(l2)])
+            rem_layers.remove(mylayers[int(l1)])
+            rem_layers.remove(mylayers[int(l2)])
+            rem_layers.append(l_new)
+            mylayers.append(l_new)
+            q = self.__compute_q_approx(rem_layers)
+            q_vals.append(q)
+        self.q_vals_approx = q_vals
+        return q_vals
+        
+    def compute_partitions(self):
+        if (self.verb):
+            sys.stderr.write("Getting partitions...\n")
+        if self.Z == None:
+            self.reduce()
+        if self.q_vals == None:
+            self.get_q_profile()
+        sets = {}
+        M = len(self.layers)
+        for i in range(len(self.layers)):
+            sets[i] = [i]
+        best_pos = self.q_vals.index(max(self.q_vals))
+        j = 0
+        cur_part = sets.values()
+        self.cuts = [copy.deepcopy(cur_part)]
+        while j < M-1:
+            l1, l2, _x, _y = self.Z[j]
+            l1 = int(l1)
+            l2 = int(l2)
+            val = sets[l1]
+            val.extend(sets[l2])
+            sets[M+j] = val
+            r1 = cur_part.index(sets[l1])
+            cur_part.pop(r1)
+            r2 = cur_part.index(sets[l2])
+            cur_part.pop(r2)
+            cur_part.append(val)
+            j += 1
+            self.cuts.append(copy.deepcopy(cur_part))
+        self.cuts.append(copy.deepcopy(cur_part))
+        return zip(self.q_vals, self.cuts)
+
+
+    def compute_partitions_approx(self):
+        if (self.verb):
+            sys.stderr.write("Getting partitions (approx)...\n")
+        if self.Z_approx == None:
+            self.reduce_approx()
+        if self.q_vals_approx == None:
+            self.get_q_profile_approx()
+        sets = {}
+        M = len(self.layers)
+        for i in range(len(self.layers)):
+            sets[i] = [i]
+        best_pos = self.q_vals_approx.index(max(self.q_vals_approx))
+        j = 0
+        cur_part = sets.values()
+        self.cuts_approx = [copy.deepcopy(cur_part)]
+        while j < M-1:
+            l1, l2, _x, _y = self.Z_approx[j]
+            l1 = int(l1)
+            l2 = int(l2)
+            val = sets[l1]
+            val.extend(sets[l2])
+            sets[M+j] = val
+            r1 = cur_part.index(sets[l1])
+            cur_part.pop(r1)
+            r2 = cur_part.index(sets[l2])
+            cur_part.pop(r2)
+            cur_part.append(val)
+            j += 1
+            self.cuts_approx.append(copy.deepcopy(cur_part))
+        self.cuts_approx.append(copy.deepcopy(cur_part))
+        return zip(self.q_vals_approx, self.cuts_approx)
+
+    def draw_dendrogram(self, force = False):
+        if not has_matplotlib:
+            sys.stderr.write("No matplotlib module found in draw_dendrogram...Exiting!!!\n")
+            sys.exit(3)
+        if self.Z == None:
+            if not force:
+                sys.stderr.write("Please call reduce() first or specify 'force=True'")
+            else:
+                self.reduce()
+        dendrogram(self.Z, no_plot=False)
+        matplotlib.pyplot.draw()
+        matplotlib.pyplot.show()
+
+    def draw_dendrogram_approx(self, force = False):
+        if not has_matplotlib:
+            sys.stderr.write("No matplotlib module found in draw_dendrogram_approx...Exiting!!!\n")
+            sys.exit(3)
+        if self.Z_approx == None:
+            if not force:
+                sys.stderr.write("Please call reduce_approx() first or specify 'force=True'")
+            else:
+                self.reduce_approx()
+        dendrogram(self.Z_approx, no_plot=False)
+        matplotlib.pyplot.draw()
+        matplotlib.pyplot.show()
+
+    def dump_partitions(self):
+        part = zip(self.q_vals, self.cuts)
+        for q, p in part:
+            print q, "->", p
+
+    def dump_partitions_approx(self):
+        part = zip(self.q_vals_approx, self.cuts_approx)
+        for q, p in part:
+            print q, "->", p
+
+
+
+
diff --git a/python/test/simple_test.py b/python/test/simple_test.py
new file mode 100644
index 0000000..e2919c6
--- /dev/null
+++ b/python/test/simple_test.py
@@ -0,0 +1,22 @@
+import multired as mr
+import sys
+
+
+if len(sys.argv) < 2:
+    print "Usage: %s <layer_list>" % sys.argv[0]
+    sys.exit(1)
+
+print "Loading layers...",
+m = mr.multiplex_red(sys.argv[1], verbose=True)
+print "[DONE]"
+
+print "Getting partitons...",
+part = m.compute_partitions()
+print "[DONE]"
+
+print "Partitions:..."
+m.dump_partitions()
+
+m.draw_dendrogram()
+
+
diff --git a/python/test/simple_test_approx.py b/python/test/simple_test_approx.py
new file mode 100644
index 0000000..fe2ab30
--- /dev/null
+++ b/python/test/simple_test_approx.py
@@ -0,0 +1,16 @@
+import multired as mr
+import sys
+
+
+if len(sys.argv) < 2:
+    print "Usage: %s <layer_list>" % sys.argv[0]
+    sys.exit(1)
+
+m = mr.multiplex_red(sys.argv[1], verbose=True, fit_degree=20)
+part = m.compute_partitions_approx()
+
+print "Partitions:..."
+m.dump_partitions_approx()
+
+m.draw_dendrogram_approx()
+
diff --git a/python/test/test.py b/python/test/test.py
new file mode 100644
index 0000000..5ccb490
--- /dev/null
+++ b/python/test/test.py
@@ -0,0 +1,34 @@
+import multired as mr
+import sys
+
+
+if len(sys.argv) < 2:
+    print "Usage: %s <layer_list>" % sys.argv[0]
+    sys.exit(1)
+
+print "Loading layers...",
+m = mr.multiplex_red(sys.argv[1])
+print "[DONE]"
+
+print "Computing layer entropies...",
+m.compute_layer_entropies()
+print "[DONE]"
+
+print "Computing JSD matrix...",
+m.compute_JSD_matrix()
+print "[DONE]"
+
+print "Performing reduction...",
+m.reduce()
+print "[DONE]"
+
+print "Getting partitons...",
+part = m.compute_partitions()
+print "[DONE]"
+
+print "Partitions:...",
+m.dump_partitions()
+print "[DONE]"
+
+
+
diff --git a/python/test/test_approx.py b/python/test/test_approx.py
new file mode 100644
index 0000000..353436c
--- /dev/null
+++ b/python/test/test_approx.py
@@ -0,0 +1,34 @@
+import multired as mr
+import sys
+
+
+if len(sys.argv) < 2:
+    print "Usage: %s <layer_list>" % sys.argv[0]
+    sys.exit(1)
+
+print "Loading layers...",
+m = mr.multiplex_red(sys.argv[1])
+print "[DONE]"
+
+print "Computing layer entropies (approx)...",
+m.compute_layer_entropies_approx()
+print "[DONE]"
+
+print "Computing JSD matrix (approx)...",
+m.compute_JSD_matrix_approx()
+print "[DONE]"
+
+print "Performing reduction (approx)...",
+m.reduce_approx()
+print "[DONE]"
+
+
+print "Getting partitons...",
+part = m.compute_partitions_approx()
+print "[DONE]"
+
+print "Partitions:..."
+m.dump_partitions_approx()
+print "[DONE]"
+
+