a first try of representations

src/representation.py

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+# -*- coding: UTF-8 -*-
+'''
+Created on 22 May 2012
+
+@author: Matthieu Zimmer
+
+'''
+from multilayerp import MultilayerPerceptron
+from data import DataFile
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+from simulation import Simulation
+import numpy as np
+
+def build_matrice(weightl, nlist, width):
+    weights = [0. for _ in range(len(nlist[0].weights))]
+
+    for i in range(len(weights)):
+        wsum = 0
+        for j in range(len(weightl)):
+            wsum += nlist[j].weights[i] * weightl[j]
+        weights[i] += wsum
+
+    show_repr(weights, width)
+
+def show_repr(weightl, width):
+    matrice = [[] for i in range(len(weightl)//width)]
+
+    vmax = max(weightl)
+    vmin = min(weightl)
+
+    for j in range(len(weightl)//width):
+        for i in range(width):
+            nw = (weightl[j*width + i] - vmin) * (1./(vmax - vmin))
+            matrice[j].append(round(nw, 2))
+
+
+    fig = plt.figure()
+    plt.clf()
+    ax = fig.add_subplot(111)
+    ax.set_aspect(1)
+    res = ax.imshow(np.array(matrice), cmap=cm.gist_gray, 
+                    interpolation='nearest')
+
+    width = len(matrice)
+    height = len(matrice[0])
+
+    for x in range(width):
+        for y in range(height):
+            ax.annotate(str(matrice[x][y]), xy=(y, x), 
+                        horizontalalignment='center',
+                        verticalalignment='center')
+
+    cb = fig.colorbar(res)
+    alphabet = '012345'
+    plt.xticks(range(width), alphabet[:width])
+    plt.yticks(range(height), alphabet[:height])
+
+
+    plt.show()
+
+
+    pass
+
+
+if __name__ == '__main__':    
+    mode = MultilayerPerceptron.R0to1
+    nbr_network = 1
+    momentum = 0.9
+    lrate = 0.1
+    nbr_try = 10
+    nbr_epoch = 1000
+
+#   Data Sample Declaration
+    def data():
+#        return DataFile("digit_handwritten_16.txt", mode)
+        return DataFile("digit_shape.txt", mode)
+
+#   Network Declaration
+    def FoN(inputs, outputs):
+        return MultilayerPerceptron(inputs, inputs//4, outputs, learning_rate=lrate, momentum=momentum, grid=mode)
+
+#   Work on one step
+    def step_propagation(network, inputs, outputs):
+        network['FoN'].calc_output(inputs)
+
+    def step_statictics(simu, network, plot, inputs, outputs):
+        #rms
+        pass
+
+    def step_learn(network, inputs, outputs):
+        #Learning
+        network['FoN'].train(inputs, outputs)
+
+    sim = Simulation(data, nbr_network, [FoN])
+    sim.dgraph(['FoN_rms'])
+    sim.launch(nbr_epoch, nbr_try, step_propagation, step_statictics, step_learn)
+
+    #show_repr(sim.networks[0]['FoN'].hiddenNeurons[0].weights, 4)
+    build_matrice(sim.networks[0]['FoN'].outputNeurons[2].weights, sim.networks[0]['FoN'].hiddenNeurons, 4)