From 96cce70e7d071929065a69735993b5d6c25d7c8c Mon Sep 17 00:00:00 2001
From: Denis Arrivault <denis.arrivault@lif.univ-mrs.fr>
Date: Fri, 2 Feb 2018 16:18:33 +0100
Subject: [PATCH] Adding a get_dot function in Automata + jupyter example
---
VERSION | 2 +-
examples/3.pautomac_light.train.dot.gv | 48 ++
examples/3.pautomac_light.train.dot.gv.pdf | Bin 0 -> 14746 bytes
examples/GraphvizTests.ipynb | 776 ++++++++++++++++++
.../graphivz-output/3.pautomac_light.train.gv | 48 ++
.../3.pautomac_light.train.gv.pdf | Bin 0 -> 14746 bytes
.../3.pautomac_light_man.train.gv | 48 ++
.../3.pautomac_light_man.train.gv.pdf | Bin 0 -> 14746 bytes
examples/graphivz-output/round-table.gv | 9 +
examples/graphivz-output/round-table.gv.pdf | Bin 0 -> 15059 bytes
setup.py | 34 -
splearn/__init__.py | 2 +-
splearn/automaton.py | 81 +-
splearn/datasets/data_sample.py | 34 -
splearn/hankel.py | 34 -
splearn/spectral.py | 34 -
splearn/tests/datasets/get_dataset_path.py | 34 -
splearn/tests/test_automaton.py | 34 -
splearn/tests/test_base.py | 34 -
splearn/tests/test_data_sample.py | 34 -
splearn/tests/test_hankel.py | 34 -
splearn/tests/test_spectral.py | 34 -
22 files changed, 978 insertions(+), 376 deletions(-)
create mode 100644 examples/3.pautomac_light.train.dot.gv
create mode 100644 examples/3.pautomac_light.train.dot.gv.pdf
create mode 100644 examples/GraphvizTests.ipynb
create mode 100644 examples/graphivz-output/3.pautomac_light.train.gv
create mode 100644 examples/graphivz-output/3.pautomac_light.train.gv.pdf
create mode 100644 examples/graphivz-output/3.pautomac_light_man.train.gv
create mode 100644 examples/graphivz-output/3.pautomac_light_man.train.gv.pdf
create mode 100644 examples/graphivz-output/round-table.gv
create mode 100644 examples/graphivz-output/round-table.gv.pdf
diff --git a/VERSION b/VERSION
index ad13d99..e19bf06 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-splearn:1.0.1
\ No newline at end of file
+splearn:1.1.0
\ No newline at end of file
diff --git a/examples/3.pautomac_light.train.dot.gv b/examples/3.pautomac_light.train.dot.gv
new file mode 100644
index 0000000..c3ac1dc
--- /dev/null
+++ b/examples/3.pautomac_light.train.dot.gv
@@ -0,0 +1,48 @@
+//3.pautomac_light.train.dot
+digraph {
+ 0 [label="0
+______
+> -0.00
+0.08 >"]
+ 1 [label="1
+______
+> 0.00
+-0.02 >"]
+ 2 [label="2
+______
+> -0.04
+-0.45 >"]
+ 3 [label="3
+______
+> -0.11
+0.63 >"]
+ 4 [label="4
+______
+> -0.09
+-0.55 >"]
+ 0 -> 1 [label="0:-0.24"]
+ 0 -> 2 [label="0:0.35"]
+ 0 -> 3 [label="0:-0.28"]
+ 0 -> 4 [label="0:-0.21"]
+ 1 -> 1 [label="0:-0.30"]
+ 1 -> 2 [label="0:0.21"]
+ 1 -> 4 [label="0:-0.56"]
+ 2 -> 3 [label="0:-0.21"]
+ 4 -> 4 [label="0:0.24"]
+ 0 -> 2 [label="1:-0.30"]
+ 0 -> 3 [label="1:0.28"]
+ 0 -> 4 [label="1:0.20"]
+ 1 -> 2 [label="1:0.26"]
+ 0 -> 2 [label="2:0.38"]
+ 0 -> 3 [label="2:-0.22"]
+ 0 -> 4 [label="2:-0.23"]
+ 1 -> 4 [label="2:0.35"]
+ 1 -> 2 [label="3:0.28"]
+ 1 -> 3 [label="3:-0.24"]
+ 2 -> 2 [label="3:-0.63"]
+ 2 -> 3 [label="3:0.47"]
+ 3 -> 2 [label="3:-0.37"]
+ 3 -> 4 [label="3:0.65"]
+ 4 -> 3 [label="3:-0.38"]
+ 4 -> 4 [label="3:0.66"]
+}
diff --git a/examples/3.pautomac_light.train.dot.gv.pdf b/examples/3.pautomac_light.train.dot.gv.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..ec0f99e07bfcdac4b0e0c21fe5c94795515f90c1
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diff --git a/examples/GraphvizTests.ipynb b/examples/GraphvizTests.ipynb
new file mode 100644
index 0000000..e5b19df
--- /dev/null
+++ b/examples/GraphvizTests.ipynb
@@ -0,0 +1,776 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from graphviz import Digraph\n",
+ "from graphviz import Source\n",
+ "from splearn.datasets.base import load_data_sample\n",
+ "from splearn.tests.datasets.get_dataset_path import get_dataset_path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_file = '3.pautomac_light.train'\n",
+ "data = load_data_sample(adr=get_dataset_path(train_file))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.nbL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "5000"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.nbEx"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Splearn_array([[ 3., 0., 3., ..., -1., -1., -1.],\n",
+ " [ 3., 3., -1., ..., -1., -1., -1.],\n",
+ " [ 3., 2., 0., ..., -1., -1., -1.],\n",
+ " ...,\n",
+ " [ 3., 1., 3., ..., -1., -1., -1.],\n",
+ " [ 3., 0., 3., ..., -1., -1., -1.],\n",
+ " [ 3., 3., 1., ..., -1., -1., -1.]])"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from splearn import Learning, Hankel , Spectral"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Start Hankel matrix computation\n",
+ "End of Hankel matrix computation\n",
+ "Start Building Automaton from Hankel matrix\n",
+ "End of Automaton computation\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Spectral(lcolumns=7, lrows=7, mode_quiet=False, partial=True, rank=5,\n",
+ " smooth_method='none', sparse=True, version='classic')"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sp = Spectral()\n",
+ "sp.fit(X=data.data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "A = sp.Automaton"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-0.00049344, 0.00306347, -0.04407393, -0.10777703, -0.08663914])"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "A.initial"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0.07757137, -0.02422029, -0.44681254, 0.62773208, -0.55467443])"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "A.final"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[array([[ 0.04512121, -0.2403897 , 0.34945 , -0.28116807, -0.21402523],\n",
+ " [ 0.06925801, -0.30062293, 0.20641375, -0.14960814, -0.55805732],\n",
+ " [ 0.02980115, -0.13866481, 0.18362213, -0.20969545, -0.14481622],\n",
+ " [ 0.00569934, -0.02338583, -0.06600665, 0.10749935, -0.15103655],\n",
+ " [-0.02008655, 0.09026348, -0.00552559, -0.03135532, 0.24329022]]),\n",
+ " array([[ 0.07744772, 0.09007074, -0.30472201, 0.27676245, 0.20289396],\n",
+ " [-0.0990298 , -0.08061847, 0.25853171, -0.1208633 , -0.11085208],\n",
+ " [-0.06171079, -0.06244152, 0.12007655, 0.00250637, -0.15679675],\n",
+ " [-0.00273697, -0.00900572, -0.00046003, -0.00855043, -0.05375465],\n",
+ " [ 0.03098733, 0.0397268 , -0.04997113, 0.00357694, 0.14182576]]),\n",
+ " array([[-0.06791915, -0.11357938, 0.37955393, -0.2178498 , -0.22977695],\n",
+ " [ 0.11596642, 0.14914957, -0.13357508, -0.00891606, 0.34841537],\n",
+ " [ 0.01173082, 0.0192738 , 0.04142658, -0.03534659, 0.02316491],\n",
+ " [ 0.00732891, 0.00553651, -0.02245608, 0.03611543, -0.03851434],\n",
+ " [-0.01058989, -0.01062662, -0.00054311, -0.02556748, 0.04984889]]),\n",
+ " array([[ 0.07276211, -0.01571956, 0.07428593, -0.10369862, 0.02475347],\n",
+ " [-0.05607105, -0.08896207, 0.27638225, -0.23711256, 0.07372294],\n",
+ " [-0.00739129, -0.0487418 , -0.62912397, 0.46816277, 0.09251699],\n",
+ " [-0.00711022, -0.05623318, -0.36606659, -0.0132978 , 0.64910332],\n",
+ " [ 0.00233552, -0.02156115, 0.09096243, -0.38438823, 0.66164772]])]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "A.transitions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'classic'"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "A.type"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "A.nbL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "5"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "A.nbS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "th = 0.2\n",
+ "wa = Digraph(comment='3.pautomac_light.train')\n",
+ "for i in range(A.nbS):\n",
+ " label = \"{0:d}\\n______\\n> {1:.2f}\\n{2:.2f} >\".format(i, A.initial[i], A.final[i])\n",
+ " wa.node(str(i),label)\n",
+ "for l in range(A.nbL):\n",
+ " for i in range(A.nbS):\n",
+ " for j in range(A.nbS):\n",
+ " weight = A.transitions[l][i,j]\n",
+ " if (np.abs(weight) > th):\n",
+ " label = \"{0:d}:{1:.2f}\".format(l,weight)\n",
+ " wa.edge(str(i), str(j), label)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "// 3.pautomac_light.train\n",
+ "digraph {\n",
+ "\t0 [label=\"0\n",
+ "______\n",
+ "> -0.00\n",
+ "0.08 >\"]\n",
+ "\t1 [label=\"1\n",
+ "______\n",
+ "> 0.00\n",
+ "-0.02 >\"]\n",
+ "\t2 [label=\"2\n",
+ "______\n",
+ "> -0.04\n",
+ "-0.45 >\"]\n",
+ "\t3 [label=\"3\n",
+ "______\n",
+ "> -0.11\n",
+ "0.63 >\"]\n",
+ "\t4 [label=\"4\n",
+ "______\n",
+ "> -0.09\n",
+ "-0.55 >\"]\n",
+ "\t0 -> 1 [label=\"0:-0.24\"]\n",
+ "\t0 -> 2 [label=\"0:0.35\"]\n",
+ "\t0 -> 3 [label=\"0:-0.28\"]\n",
+ "\t0 -> 4 [label=\"0:-0.21\"]\n",
+ "\t1 -> 1 [label=\"0:-0.30\"]\n",
+ "\t1 -> 2 [label=\"0:0.21\"]\n",
+ "\t1 -> 4 [label=\"0:-0.56\"]\n",
+ "\t2 -> 3 [label=\"0:-0.21\"]\n",
+ "\t4 -> 4 [label=\"0:0.24\"]\n",
+ "\t0 -> 2 [label=\"1:-0.30\"]\n",
+ "\t0 -> 3 [label=\"1:0.28\"]\n",
+ "\t0 -> 4 [label=\"1:0.20\"]\n",
+ "\t1 -> 2 [label=\"1:0.26\"]\n",
+ "\t0 -> 2 [label=\"2:0.38\"]\n",
+ "\t0 -> 3 [label=\"2:-0.22\"]\n",
+ "\t0 -> 4 [label=\"2:-0.23\"]\n",
+ "\t1 -> 4 [label=\"2:0.35\"]\n",
+ "\t1 -> 2 [label=\"3:0.28\"]\n",
+ "\t1 -> 3 [label=\"3:-0.24\"]\n",
+ "\t2 -> 2 [label=\"3:-0.63\"]\n",
+ "\t2 -> 3 [label=\"3:0.47\"]\n",
+ "\t3 -> 2 [label=\"3:-0.37\"]\n",
+ "\t3 -> 4 [label=\"3:0.65\"]\n",
+ "\t4 -> 3 [label=\"3:-0.38\"]\n",
+ "\t4 -> 4 [label=\"3:0.66\"]\n",
+ "}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(wa.source)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "//automata.dot\n",
+ "digraph {\n",
+ "\t0 [label=\"0\n",
+ "______\n",
+ "> -0.00\n",
+ "0.08 >\"]\n",
+ "\t1 [label=\"1\n",
+ "______\n",
+ "> 0.00\n",
+ "-0.02 >\"]\n",
+ "\t2 [label=\"2\n",
+ "______\n",
+ "> -0.04\n",
+ "-0.45 >\"]\n",
+ "\t3 [label=\"3\n",
+ "______\n",
+ "> -0.11\n",
+ "0.63 >\"]\n",
+ "\t4 [label=\"4\n",
+ "______\n",
+ "> -0.09\n",
+ "-0.55 >\"]\n",
+ "\t0 -> 1 [label=\"0:-0.24\"]\n",
+ "\t0 -> 2 [label=\"0:0.35\"]\n",
+ "\t0 -> 3 [label=\"0:-0.28\"]\n",
+ "\t0 -> 4 [label=\"0:-0.21\"]\n",
+ "\t1 -> 1 [label=\"0:-0.30\"]\n",
+ "\t1 -> 2 [label=\"0:0.21\"]\n",
+ "\t1 -> 4 [label=\"0:-0.56\"]\n",
+ "\t2 -> 3 [label=\"0:-0.21\"]\n",
+ "\t4 -> 4 [label=\"0:0.24\"]\n",
+ "\t0 -> 2 [label=\"1:-0.30\"]\n",
+ "\t0 -> 3 [label=\"1:0.28\"]\n",
+ "\t0 -> 4 [label=\"1:0.20\"]\n",
+ "\t1 -> 2 [label=\"1:0.26\"]\n",
+ "\t0 -> 2 [label=\"2:0.38\"]\n",
+ "\t0 -> 3 [label=\"2:-0.22\"]\n",
+ "\t0 -> 4 [label=\"2:-0.23\"]\n",
+ "\t1 -> 4 [label=\"2:0.35\"]\n",
+ "\t1 -> 2 [label=\"3:0.28\"]\n",
+ "\t1 -> 3 [label=\"3:-0.24\"]\n",
+ "\t2 -> 2 [label=\"3:-0.63\"]\n",
+ "\t2 -> 3 [label=\"3:0.47\"]\n",
+ "\t3 -> 2 [label=\"3:-0.37\"]\n",
+ "\t3 -> 4 [label=\"3:0.65\"]\n",
+ "\t4 -> 3 [label=\"3:-0.38\"]\n",
+ "\t4 -> 4 [label=\"3:0.66\"]\n",
+ "}\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "nb_dec = 2\n",
+ "threshold = th\n",
+ "filename = \"automata.dot\"\n",
+ "prec = \".{:d}f\".format(nb_dec)\n",
+ "out = \"//{:s}\\ndigraph {{\\n\".format(filename)\n",
+ "for i in range(A.nbS):\n",
+ " label = \"{0:d}\\n______\\n> {1:\" + prec + \"}\\n{2:\" + prec + \"} >\"\n",
+ " label = label.format(i, A.initial[i], A.final[i])\n",
+ " out += \"\\t{0:d} [label=\\\"\".format(i)\n",
+ " out += label + \"\\\"]\\n\"\n",
+ "for l in range(A.nbL):\n",
+ " for i in range(A.nbS):\n",
+ " for j in range(A.nbS):\n",
+ " weight = A.transitions[l][i,j]\n",
+ " if (np.abs(weight) > threshold):\n",
+ " label = \"{0:d}:{1:\" + prec + \"}\"\n",
+ " label = label.format(l,weight)\n",
+ " out += \"\\t{0:d} -> {1:d} [label=\\\"\".format(i,j)\n",
+ " out += label + \"\\\"]\\n\"\n",
+ "out += \"}\\n\"\n",
+ "print(out)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+ "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+ " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+ "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n",
+ " -->\n",
+ "<!-- Title: %3 Pages: 1 -->\n",
+ "<svg width=\"769pt\" height=\"693pt\"\n",
+ " viewBox=\"0.00 0.00 769.23 692.83\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+ "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 688.833)\">\n",
+ "<title>%3</title>\n",
+ "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-688.833 765.226,-688.833 765.226,4 -4,4\"/>\n",
+ "<!-- 0 -->\n",
+ "<g id=\"node1\" class=\"node\"><title>0</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"454.226\" cy=\"-636.749\" rx=\"40.1111\" ry=\"48.1667\"/>\n",
+ "<text text-anchor=\"middle\" x=\"454.226\" y=\"-655.549\" font-family=\"Times,serif\" font-size=\"14.00\">0</text>\n",
+ "<text text-anchor=\"middle\" x=\"454.226\" y=\"-640.549\" font-family=\"Times,serif\" font-size=\"14.00\">______</text>\n",
+ "<text text-anchor=\"middle\" x=\"454.226\" y=\"-625.549\" font-family=\"Times,serif\" font-size=\"14.00\">> -0.00</text>\n",
+ "<text text-anchor=\"middle\" x=\"454.226\" y=\"-610.549\" font-family=\"Times,serif\" font-size=\"14.00\">0.08 ></text>\n",
+ "</g>\n",
+ "<!-- 1 -->\n",
+ "<g id=\"node2\" class=\"node\"><title>1</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"91.2259\" cy=\"-489.583\" rx=\"40.1111\" ry=\"48.1667\"/>\n",
+ "<text text-anchor=\"middle\" x=\"91.2259\" y=\"-508.383\" font-family=\"Times,serif\" font-size=\"14.00\">1</text>\n",
+ "<text text-anchor=\"middle\" x=\"91.2259\" y=\"-493.383\" font-family=\"Times,serif\" font-size=\"14.00\">______</text>\n",
+ "<text text-anchor=\"middle\" x=\"91.2259\" y=\"-478.383\" font-family=\"Times,serif\" font-size=\"14.00\">> 0.00</text>\n",
+ "<text text-anchor=\"middle\" x=\"91.2259\" y=\"-463.383\" font-family=\"Times,serif\" font-size=\"14.00\">-0.02 ></text>\n",
+ "</g>\n",
+ "<!-- 0->1 -->\n",
+ "<g id=\"edge1\" class=\"edge\"><title>0->1</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M413.762,-633.458C356.474,-628.716 249.94,-614.281 171.226,-570.666 154.111,-561.183 138.209,-546.963 125.164,-533.125\"/>\n",
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+ "<text text-anchor=\"middle\" x=\"191.226\" y=\"-559.466\" font-family=\"Times,serif\" font-size=\"14.00\">0:-0.24</text>\n",
+ "</g>\n",
+ "<!-- 2 -->\n",
+ "<g id=\"node3\" class=\"node\"><title>2</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"271.226\" cy=\"-342.416\" rx=\"40.1111\" ry=\"48.1667\"/>\n",
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+ ],
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+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "wa.render('graphivz-output/3.pautomac_light.train.gv', view=False)\n",
+ "Source.from_file(\"graphivz-output/3.pautomac_light.train.gv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "src = Source(out)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'graphivz-output/3.pautomac_light_man.train.gv.pdf'"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "src.render('graphivz-output/3.pautomac_light_man.train.gv', view=True) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Start Hankel matrix computation\n",
+ "End of Hankel matrix computation\n",
+ "Start Building Automaton from Hankel matrix\n",
+ "End of Automaton computation\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'3.pautomac_light.train.dot.gv.pdf'"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from splearn.datasets.base import load_data_sample\n",
+ "from splearn.tests.datasets.get_dataset_path import get_dataset_path\n",
+ "from splearn import Spectral\n",
+ "train_file = '3.pautomac_light.train'\n",
+ "data = load_data_sample(adr=get_dataset_path(train_file))\n",
+ "sp = Spectral()\n",
+ "sp.fit(X=data.data)\n",
+ "dotfile = \"3.pautomac_light.train.dot\"\n",
+ "dot = sp.Automaton.get_dot(threshold = 0.2, title = dotfile)\n",
+ "# To display the dot string one can use graphviz:\n",
+ "from graphviz import Source\n",
+ "src = Source(dot)\n",
+ "src.render(dotfile + '.gv', view=True) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/graphivz-output/3.pautomac_light.train.gv b/examples/graphivz-output/3.pautomac_light.train.gv
new file mode 100644
index 0000000..4e539a2
--- /dev/null
+++ b/examples/graphivz-output/3.pautomac_light.train.gv
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+}
diff --git a/examples/graphivz-output/3.pautomac_light.train.gv.pdf b/examples/graphivz-output/3.pautomac_light.train.gv.pdf
new file mode 100644
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diff --git a/examples/graphivz-output/3.pautomac_light_man.train.gv b/examples/graphivz-output/3.pautomac_light_man.train.gv
new file mode 100644
index 0000000..d0bd11b
--- /dev/null
+++ b/examples/graphivz-output/3.pautomac_light_man.train.gv
@@ -0,0 +1,48 @@
+//automata.dot
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+ 0 -> 3 [label="0:-0.28"]
+ 0 -> 4 [label="0:-0.21"]
+ 1 -> 1 [label="0:-0.30"]
+ 1 -> 2 [label="0:0.21"]
+ 1 -> 4 [label="0:-0.56"]
+ 2 -> 3 [label="0:-0.21"]
+ 4 -> 4 [label="0:0.24"]
+ 0 -> 2 [label="1:-0.30"]
+ 0 -> 3 [label="1:0.28"]
+ 0 -> 4 [label="1:0.20"]
+ 1 -> 2 [label="1:0.26"]
+ 0 -> 2 [label="2:0.38"]
+ 0 -> 3 [label="2:-0.22"]
+ 0 -> 4 [label="2:-0.23"]
+ 1 -> 4 [label="2:0.35"]
+ 1 -> 2 [label="3:0.28"]
+ 1 -> 3 [label="3:-0.24"]
+ 2 -> 2 [label="3:-0.63"]
+ 2 -> 3 [label="3:0.47"]
+ 3 -> 2 [label="3:-0.37"]
+ 3 -> 4 [label="3:0.65"]
+ 4 -> 3 [label="3:-0.38"]
+ 4 -> 4 [label="3:0.66"]
+}
diff --git a/examples/graphivz-output/3.pautomac_light_man.train.gv.pdf b/examples/graphivz-output/3.pautomac_light_man.train.gv.pdf
new file mode 100644
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HcmV?d00001
diff --git a/examples/graphivz-output/round-table.gv b/examples/graphivz-output/round-table.gv
new file mode 100644
index 0000000..48aa015
--- /dev/null
+++ b/examples/graphivz-output/round-table.gv
@@ -0,0 +1,9 @@
+// The Round Table
+digraph {
+ A [label="King Arthur"]
+ B [label="Sir Bedevere the Wise"]
+ L [label="Sir Lancelot the Brave"]
+ A -> B
+ A -> L
+ B -> L [constraint=false]
+}
diff --git a/examples/graphivz-output/round-table.gv.pdf b/examples/graphivz-output/round-table.gv.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..ed7810cf4f968b2f4ccd117b59a3e01137c243bd
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diff --git a/setup.py b/setup.py
index 66e014e..cbb1d50 100755
--- a/setup.py
+++ b/setup.py
@@ -1,39 +1,5 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
-# ######### COPYRIGHT #########
-#
-# Copyright(c) 2016
-# -----------------
-#
-# * LabEx Archimède: http://labex-archimede.univ-amu.fr/
-# * Laboratoire d'Informatique Fondamentale : http://www.lif.univ-mrs.fr/
-#
-# Contributors:
-# ------------
-#
-# * François Denis <francois.denis_AT_lif.univ-mrs.fr>
-# * Rémy Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
-# * Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
-# * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
-#
-# Description:
-# -----------
-#
-# scitkit-splearn is a toolbox in
-# python for spectral learning algorithms.
-#
-# Version:
-# -------
-#
-# * splearn version = 1.0.1
-#
-# Licence:
-# -------
-#
-# License: 3-clause BSD
-#
-#
-# ######### COPYRIGHT #########
from __future__ import print_function
import os
diff --git a/splearn/__init__.py b/splearn/__init__.py
index 056f036..384681e 100644
--- a/splearn/__init__.py
+++ b/splearn/__init__.py
@@ -2,4 +2,4 @@ from splearn.automaton import Automaton
from splearn.spectral import Learning
from splearn.spectral import Spectral
from splearn.hankel import Hankel
-__version__ = "1.0.1"
+__version__ = "1.1.0"
diff --git a/splearn/automaton.py b/splearn/automaton.py
index 98375c8..44338b9 100644
--- a/splearn/automaton.py
+++ b/splearn/automaton.py
@@ -1,38 +1,4 @@
# -*- coding: utf-8 -*-
-# ######### COPYRIGHT #########
-#
-# Copyright(c) 2016
-# -----------------
-#
-# * LabEx Archimède: http://labex-archimede.univ-amu.fr/
-# * Laboratoire d'Informatique Fondamentale : http://www.lif.univ-mrs.fr/
-#
-# Contributors:
-# ------------
-#
-# * François Denis <francois.denis_AT_lif.univ-mrs.fr>
-# * Rémy Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
-# * Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
-# * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
-#
-# Description:
-# -----------
-#
-# scitkit-splearn is a toolbox in
-# python for spectral learning algorithms.
-#
-# Version:
-# -------
-#
-# * splearn version = 1.0.1
-#
-# Licence:
-# -------
-#
-# License: 3-clause BSD
-#
-#
-# ######### COPYRIGHT #########
"""This module contains the Automaton class
"""
@@ -639,3 +605,50 @@ class Automaton(object):
toReturn.update({i: max(w, 0)})
toReturn.update({-1: max(p_w, 0)})
return toReturn
+
+ def get_dot(self, threshold = 0., nb_dec = 2, title = "Weighted Automata"):
+ """ Return a string that contains the Automata into dot (graphviz) format
+ :Example:
+
+ >>> from splearn.datasets.base import load_data_sample
+ >>> from splearn.tests.datasets.get_dataset_path import get_dataset_path
+ >>> from splearn import Spectral
+ >>> train_file = '3.pautomac_light.train'
+ >>> data = load_data_sample(adr=get_dataset_path(train_file))
+ >>> sp = Spectral()
+ >>> sp.fit(X=data.data)
+ >>> dotfile = "3.pautomac_light.train.dot"
+ >>> dot = sp.Automaton.get_dot(threshold = 0.2, title = dotfile)
+ >>> # To display the dot string one can use graphviz:
+ >>> from graphviz import Source
+ >>> src = Source(dot)
+ >>> src.render(dotfile + '.gv', view=True)
+
+ - Input:
+
+ :param Automaton self
+ :param float threshold for the value to keep. If |weight| < threshold, the
+ corresponding transition is not kept as an edge in the final dot string.
+ :param int nb_dec is the number of decimals to keep for the weights.
+ :param string title corresponds to the top comment of the string
+
+ :returns: a string with the current Automata in dot format
+ """
+ prec = ".{:d}f".format(nb_dec)
+ out = "//{:s}\ndigraph {{\n".format(title)
+ for i in range(self.nbS):
+ label = "{0:d}\n______\n> {1:" + prec + "}\n{2:" + prec + "} >"
+ label = label.format(i, self.initial[i], self.final[i])
+ out += "\t{0:d} [label=\"".format(i)
+ out += label + "\"]\n"
+ for l in range(self.nbL):
+ for i in range(self.nbS):
+ for j in range(self.nbS):
+ weight = self.transitions[l][i,j]
+ if (np.abs(weight) > threshold):
+ label = "{0:d}:{1:" + prec + "}"
+ label = label.format(l,weight)
+ out += "\t{0:d} -> {1:d} [label=\"".format(i,j)
+ out += label + "\"]\n"
+ out += "}\n"
+ return out
diff --git a/splearn/datasets/data_sample.py b/splearn/datasets/data_sample.py
index 1305b04..26746e3 100644
--- a/splearn/datasets/data_sample.py
+++ b/splearn/datasets/data_sample.py
@@ -1,38 +1,4 @@
# -*- coding: utf-8 -*-
-# ######### COPYRIGHT #########
-#
-# Copyright(c) 2016
-# -----------------
-#
-# * LabEx Archimède: http://labex-archimede.univ-amu.fr/
-# * Laboratoire d'Informatique Fondamentale : http://www.lif.univ-mrs.fr/
-#
-# Contributors:
-# ------------
-#
-# * François Denis <francois.denis_AT_lif.univ-mrs.fr>
-# * Rémy Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
-# * Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
-# * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
-#
-# Description:
-# -----------
-#
-# scitkit-splearn is a toolbox in
-# python for spectral learning algorithms.
-#
-# Version:
-# -------
-#
-# * splearn version = 1.0.1
-#
-# Licence:
-# -------
-#
-# License: 3-clause BSD
-#
-#
-# ######### COPYRIGHT #########
"""This module contains the DataSample class and Splearn_array class
The DataSample class encapsulates a sample 's components
nbL and nbEx numbers,
diff --git a/splearn/hankel.py b/splearn/hankel.py
index 446d4a5..b20f7bd 100644
--- a/splearn/hankel.py
+++ b/splearn/hankel.py
@@ -1,38 +1,4 @@
# -*- coding: utf-8 -*-
-# ######### COPYRIGHT #########
-#
-# Copyright(c) 2016
-# -----------------
-#
-# * LabEx Archimède: http://labex-archimede.univ-amu.fr/
-# * Laboratoire d'Informatique Fondamentale : http://www.lif.univ-mrs.fr/
-#
-# Contributors:
-# ------------
-#
-# * François Denis <francois.denis_AT_lif.univ-mrs.fr>
-# * Rémy Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
-# * Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
-# * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
-#
-# Description:
-# -----------
-#
-# scitkit-splearn is a toolbox in
-# python for spectral learning algorithms.
-#
-# Version:
-# -------
-#
-# * splearn version = 1.0.1
-#
-# Licence:
-# -------
-#
-# License: 3-clause BSD
-#
-#
-# ######### COPYRIGHT #########
"""This module contains the Hankel class
"""
diff --git a/splearn/spectral.py b/splearn/spectral.py
index 64e4a02..ce8e027 100644
--- a/splearn/spectral.py
+++ b/splearn/spectral.py
@@ -1,38 +1,4 @@
# -*- coding: utf-8 -*-
-# ######### COPYRIGHT #########
-#
-# Copyright(c) 2016
-# -----------------
-#
-# * LabEx Archimède: http://labex-archimede.univ-amu.fr/
-# * Laboratoire d'Informatique Fondamentale : http://www.lif.univ-mrs.fr/
-#
-# Contributors:
-# ------------
-#
-# * François Denis <francois.denis_AT_lif.univ-mrs.fr>
-# * Rémy Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
-# * Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
-# * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
-#
-# Description:
-# -----------
-#
-# scitkit-splearn is a toolbox in
-# python for spectral learning algorithms.
-#
-# Version:
-# -------
-#
-# * splearn version = 1.0.1
-#
-# Licence:
-# -------
-#
-# License: 3-clause BSD
-#
-#
-# ######### COPYRIGHT #########
"""This module contains the Spectral and Learning class
.. module author:: François Denis
diff --git a/splearn/tests/datasets/get_dataset_path.py b/splearn/tests/datasets/get_dataset_path.py
index 1a37ebb..f497951 100644
--- a/splearn/tests/datasets/get_dataset_path.py
+++ b/splearn/tests/datasets/get_dataset_path.py
@@ -1,38 +1,4 @@
# -*- coding: utf-8 -*-
-# ######### COPYRIGHT #########
-#
-# Copyright(c) 2016
-# -----------------
-#
-# * LabEx Archimède: http://labex-archimede.univ-amu.fr/
-# * Laboratoire d'Informatique Fondamentale : http://www.lif.univ-mrs.fr/
-#
-# Contributors:
-# ------------
-#
-# * François Denis <francois.denis_AT_lif.univ-mrs.fr>
-# * Rémy Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
-# * Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
-# * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
-#
-# Description:
-# -----------
-#
-# scitkit-splearn is a toolbox in
-# python for spectral learning algorithms.
-#
-# Version:
-# -------
-#
-# * splearn version = 1.0.1
-#
-# Licence:
-# -------
-#
-# License: 3-clause BSD
-#
-#
-# ######### COPYRIGHT #########
"""Module to get the absolute path of a reference dataset for tests
.. moduleauthor:: Denis Arrivault
diff --git a/splearn/tests/test_automaton.py b/splearn/tests/test_automaton.py
index c9df0f8..69dd1e8 100644
--- a/splearn/tests/test_automaton.py
+++ b/splearn/tests/test_automaton.py
@@ -1,38 +1,4 @@
# -*- coding: utf-8 -*-
-# ######### COPYRIGHT #########
-#
-# Copyright(c) 2016
-# -----------------
-#
-# * LabEx Archimède: http://labex-archimede.univ-amu.fr/
-# * Laboratoire d'Informatique Fondamentale : http://www.lif.univ-mrs.fr/
-#
-# Contributors:
-# ------------
-#
-# * François Denis <francois.denis_AT_lif.univ-mrs.fr>
-# * Rémy Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
-# * Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
-# * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
-#
-# Description:
-# -----------
-#
-# scitkit-splearn is a toolbox in
-# python for spectral learning algorithms.
-#
-# Version:
-# -------
-#
-# * splearn version = 1.0.1
-#
-# Licence:
-# -------
-#
-# License: 3-clause BSD
-#
-#
-# ######### COPYRIGHT #########
from __future__ import division, print_function
import unittest
import numpy as np
diff --git a/splearn/tests/test_base.py b/splearn/tests/test_base.py
index e7684c0..1fbe062 100644
--- a/splearn/tests/test_base.py
+++ b/splearn/tests/test_base.py
@@ -1,38 +1,4 @@
# -*- coding: utf-8 -*-
-# ######### COPYRIGHT #########
-#
-# Copyright(c) 2016
-# -----------------
-#
-# * LabEx Archimède: http://labex-archimede.univ-amu.fr/
-# * Laboratoire d'Informatique Fondamentale : http://www.lif.univ-mrs.fr/
-#
-# Contributors:
-# ------------
-#
-# * François Denis <francois.denis_AT_lif.univ-mrs.fr>
-# * Rémy Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
-# * Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
-# * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
-#
-# Description:
-# -----------
-#
-# scitkit-splearn is a toolbox in
-# python for spectral learning algorithms.
-#
-# Version:
-# -------
-#
-# * splearn version = 1.0.1
-#
-# Licence:
-# -------
-#
-# License: 3-clause BSD
-#
-#
-# ######### COPYRIGHT #########
from __future__ import division, print_function
import numpy as np
diff --git a/splearn/tests/test_data_sample.py b/splearn/tests/test_data_sample.py
index f7cf18f..326ca5f 100644
--- a/splearn/tests/test_data_sample.py
+++ b/splearn/tests/test_data_sample.py
@@ -1,38 +1,4 @@
# -*- coding: utf-8 -*-
-# ######### COPYRIGHT #########
-#
-# Copyright(c) 2016
-# -----------------
-#
-# * LabEx Archimède: http://labex-archimede.univ-amu.fr/
-# * Laboratoire d'Informatique Fondamentale : http://www.lif.univ-mrs.fr/
-#
-# Contributors:
-# ------------
-#
-# * François Denis <francois.denis_AT_lif.univ-mrs.fr>
-# * Rémy Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
-# * Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
-# * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
-#
-# Description:
-# -----------
-#
-# scitkit-splearn is a toolbox in
-# python for spectral learning algorithms.
-#
-# Version:
-# -------
-#
-# * splearn version = 1.0.1
-#
-# Licence:
-# -------
-#
-# License: 3-clause BSD
-#
-#
-# ######### COPYRIGHT #########
from __future__ import division, print_function
import numpy as np
diff --git a/splearn/tests/test_hankel.py b/splearn/tests/test_hankel.py
index 86d4865..8e6e34e 100644
--- a/splearn/tests/test_hankel.py
+++ b/splearn/tests/test_hankel.py
@@ -1,38 +1,4 @@
# -*- coding: utf-8 -*-
-# ######### COPYRIGHT #########
-#
-# Copyright(c) 2016
-# -----------------
-#
-# * LabEx Archimède: http://labex-archimede.univ-amu.fr/
-# * Laboratoire d'Informatique Fondamentale : http://www.lif.univ-mrs.fr/
-#
-# Contributors:
-# ------------
-#
-# * François Denis <francois.denis_AT_lif.univ-mrs.fr>
-# * Rémy Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
-# * Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
-# * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
-#
-# Description:
-# -----------
-#
-# scitkit-splearn is a toolbox in
-# python for spectral learning algorithms.
-#
-# Version:
-# -------
-#
-# * splearn version = 1.0.1
-#
-# Licence:
-# -------
-#
-# License: 3-clause BSD
-#
-#
-# ######### COPYRIGHT #########
from __future__ import division, print_function
import unittest
diff --git a/splearn/tests/test_spectral.py b/splearn/tests/test_spectral.py
index b0c9bee..ce71a71 100644
--- a/splearn/tests/test_spectral.py
+++ b/splearn/tests/test_spectral.py
@@ -1,38 +1,4 @@
# -*- coding: utf-8 -*-
-# ######### COPYRIGHT #########
-#
-# Copyright(c) 2016
-# -----------------
-#
-# * LabEx Archimède: http://labex-archimede.univ-amu.fr/
-# * Laboratoire d'Informatique Fondamentale : http://www.lif.univ-mrs.fr/
-#
-# Contributors:
-# ------------
-#
-# * François Denis <francois.denis_AT_lif.univ-mrs.fr>
-# * Rémy Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
-# * Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
-# * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
-#
-# Description:
-# -----------
-#
-# scitkit-splearn is a toolbox in
-# python for spectral learning algorithms.
-#
-# Version:
-# -------
-#
-# * splearn version = 1.0.1
-#
-# Licence:
-# -------
-#
-# License: 3-clause BSD
-#
-#
-# ######### COPYRIGHT #########
from __future__ import division, print_function
import numpy as np
--
GitLab