diff --git a/COPYING b/COPYING
index 001fbed973710f618c7037c192cf333881dad365..947f926a70f71e318f6f8d7ede626ed028a745fd 100644
--- a/COPYING
+++ b/COPYING
@@ -1,6 +1,6 @@
New BSD License
-Copyright (c) DATE, The SCIKIT-SPLEARN developers.
+Copyright (c) 2016-DATE, The SCIKIT-SPLEARN developers.
All rights reserved.
Redistribution and use in source and binary forms, with or without
diff --git a/copyrightstamp.txt b/copyrightstamp.txt
index 05ecaeef72f9ee611ff216ccb0104ff5418901eb..a87735f6f0d5181d641ccbe2ce32aca960fe5cfc 100644
--- a/copyrightstamp.txt
+++ b/copyrightstamp.txt
@@ -1,17 +1,17 @@
######### COPYRIGHT #########
-Copyright(c) DATE
+Copyright(c) 2016-DATE
-----------------
* LabEx Archimède: http://labex-archimede.univ-amu.fr/
-* Laboratoire d'Informatique Fondamentale : http://www.lif.univ-mrs.fr/
+* Laboratoire d'Informatique et Systèmes : http://www.lis-lab.fr/
Contributors:
------------
-* François Denis <francois.denis_AT_lif.univ-mrs.fr>
-* Rémi Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
-* Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
+* François Denis <francois.denis_AT_lis-lab.fr>
+* Rémi Eyraud <remi.eyraud_AT_lis-lab.fr>
+* Denis Arrivault <contact.dev_AT_lis-lab.fr>
* Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
Description:
diff --git a/examples/3.pautomac_light.train.json b/examples/3.pautomac_light.train.json
new file mode 100644
index 0000000000000000000000000000000000000000..e77f61549383993bfd88d1693caf1b8a49cd23f1
--- /dev/null
+++ b/examples/3.pautomac_light.train.json
@@ -0,0 +1 @@
+{"automaton": {"transitions": [{"numpy.ndarray": {"values": [[0.045121209595118526, -0.24038969827843812, 0.34944999592135073, -0.2811680730534614, -0.2140252337749723], [0.0692580056243754, -0.30062293462828926, 0.20641375368520323, -0.14960814319757162, -0.5580573163749147], [0.029801151921765866, -0.13866480809160275, 0.18362212572805375, -0.20969545230657946, -0.14481622025561033], [0.005699344003197941, -0.023385825120200612, -0.0660066537398175, 0.10749935271465784, -0.1510365460415996], [-0.020086551931478853, 0.09026347555230403, -0.005525585655539282, -0.03135531709030631, 0.24329022420477361]], "dtype": "float64"}}, {"numpy.ndarray": {"values": [[0.07744772079170577, 0.09007073705761888, -0.3047220063293004, 0.2767624549859125, 0.2028939603062788], [-0.0990298048367086, -0.08061846818727912, 0.2585317069225045, -0.12086330214608965, -0.11085207725068019], [-0.061710792028537555, -0.06244151779954693, 0.12007654564862041, 0.0025063746277926833, -0.15679674731455712], [-0.002736973749965318, -0.009005721984277385, -0.0004600329590919212, -0.008550426472005884, -0.05375464678968064], [0.030987327588710818, 0.03972680066723204, -0.049971133509102365, 0.0035769411874977224, 0.14182576205856343]], "dtype": "float64"}}, {"numpy.ndarray": {"values": [[-0.06791915236220329, -0.1135793765908807, 0.3795539260405443, -0.21784979894047046, -0.22977695089937855], [0.11596642335411368, 0.14914956804629215, -0.1335750837668692, -0.008916063072030857, 0.3484153673774847], [0.011730817547426591, 0.01927380053195553, 0.04142658345867102, -0.03534658856098166, 0.02316491010895659], [0.0073289110755416255, 0.005536509132796289, -0.02245608295066666, 0.03611543477693135, -0.03851433900140641], [-0.010589894686551481, -0.010626616553723545, -0.0005431056456618302, -0.02556747670016023, 0.04984888818929077]], "dtype": "float64"}}, {"numpy.ndarray": {"values": [[0.0727621142778044, -0.01571955768557908, 0.07428592814589835, -0.10369861539250237, 0.024753473688334762], [-0.05607105449779192, -0.08896207276035564, 0.2763822539752204, -0.23711255828386232, 0.07372294122304948], [-0.007391294007753285, -0.04874179796387578, -0.6291239733858517, 0.4681627652157708, 0.09251699239093028], [-0.007110224931878148, -0.056233177358980875, -0.3660665856762066, -0.013297798115226096, 0.649103317749269], [0.0023355150085570832, -0.021561151264484168, 0.09096243479437624, -0.3843882349306193, 0.6616477207948629]], "dtype": "float64"}}], "type": "classic", "nbL": 4, "nbS": 5, "initial": {"numpy.ndarray": {"values": [-0.0004934419970497477, 0.003063469710791532, -0.0440739320155803, -0.10777702616547354, -0.0866391379316936], "dtype": "float64"}}, "final": {"numpy.ndarray": {"values": [0.07757136847945034, -0.02422029400314446, -0.4468125366321277, 0.6277320840897538, -0.554674433356226], "dtype": "float64"}}}}
\ No newline at end of file
diff --git a/examples/3.pautomac_light.train.yaml b/examples/3.pautomac_light.train.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..125acbb420b9067bb9ee4097e4ed5312dc268f64
--- /dev/null
+++ b/examples/3.pautomac_light.train.yaml
@@ -0,0 +1,67 @@
+automaton:
+ final:
+ numpy.ndarray:
+ dtype: float64
+ values: [0.07757136847945034, -0.02422029400314446, -0.4468125366321277, 0.6277320840897538,
+ -0.554674433356226]
+ initial:
+ numpy.ndarray:
+ dtype: float64
+ values: [-0.0004934419970497477, 0.003063469710791532, -0.0440739320155803,
+ -0.10777702616547354, -0.0866391379316936]
+ nbL: 4
+ nbS: 5
+ transitions:
+ - numpy.ndarray:
+ dtype: float64
+ values:
+ - [0.045121209595118526, -0.24038969827843812, 0.34944999592135073, -0.2811680730534614,
+ -0.2140252337749723]
+ - [0.0692580056243754, -0.30062293462828926, 0.20641375368520323, -0.14960814319757162,
+ -0.5580573163749147]
+ - [0.029801151921765866, -0.13866480809160275, 0.18362212572805375, -0.20969545230657946,
+ -0.14481622025561033]
+ - [0.005699344003197941, -0.023385825120200612, -0.0660066537398175, 0.10749935271465784,
+ -0.1510365460415996]
+ - [-0.020086551931478853, 0.09026347555230403, -0.005525585655539282, -0.03135531709030631,
+ 0.24329022420477361]
+ - numpy.ndarray:
+ dtype: float64
+ values:
+ - [0.07744772079170577, 0.09007073705761888, -0.3047220063293004, 0.2767624549859125,
+ 0.2028939603062788]
+ - [-0.0990298048367086, -0.08061846818727912, 0.2585317069225045, -0.12086330214608965,
+ -0.11085207725068019]
+ - [-0.061710792028537555, -0.06244151779954693, 0.12007654564862041, 0.0025063746277926833,
+ -0.15679674731455712]
+ - [-0.002736973749965318, -0.009005721984277385, -0.0004600329590919212, -0.008550426472005884,
+ -0.05375464678968064]
+ - [0.030987327588710818, 0.03972680066723204, -0.049971133509102365, 0.0035769411874977224,
+ 0.14182576205856343]
+ - numpy.ndarray:
+ dtype: float64
+ values:
+ - [-0.06791915236220329, -0.1135793765908807, 0.3795539260405443, -0.21784979894047046,
+ -0.22977695089937855]
+ - [0.11596642335411368, 0.14914956804629215, -0.1335750837668692, -0.008916063072030857,
+ 0.3484153673774847]
+ - [0.011730817547426591, 0.01927380053195553, 0.04142658345867102, -0.03534658856098166,
+ 0.02316491010895659]
+ - [0.0073289110755416255, 0.005536509132796289, -0.02245608295066666, 0.03611543477693135,
+ -0.03851433900140641]
+ - [-0.010589894686551481, -0.010626616553723545, -0.0005431056456618302, -0.02556747670016023,
+ 0.04984888818929077]
+ - numpy.ndarray:
+ dtype: float64
+ values:
+ - [0.0727621142778044, -0.01571955768557908, 0.07428592814589835, -0.10369861539250237,
+ 0.024753473688334762]
+ - [-0.05607105449779192, -0.08896207276035564, 0.2763822539752204, -0.23711255828386232,
+ 0.07372294122304948]
+ - [-0.007391294007753285, -0.04874179796387578, -0.6291239733858517, 0.4681627652157708,
+ 0.09251699239093028]
+ - [-0.007110224931878148, -0.056233177358980875, -0.3660665856762066, -0.013297798115226096,
+ 0.649103317749269]
+ - [0.0023355150085570832, -0.021561151264484168, 0.09096243479437624, -0.3843882349306193,
+ 0.6616477207948629]
+ type: classic
diff --git a/examples/automaton_io.py b/examples/automaton_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..59559e3970797bf427256a106a6a6fbfcf859998
--- /dev/null
+++ b/examples/automaton_io.py
@@ -0,0 +1,11 @@
+from splearn.automaton import Automaton
+from graphviz import Source
+
+input_file = 'simple_automata'
+format = ['json', 'yaml']
+for f in format:
+ A = Automaton.read(input_file + "." + f, format=f)
+ dot = A.get_dot(title = "Simple Automata")
+ src = Source(dot)
+ src.render(input_file + '.' + f + '.gv', view=True)
+
diff --git a/examples/json_save.ipynb b/examples/json_save.ipynb
index 5acc82882948dcfa4fbf7f2abd7666ed6ce714c5..1af50f640ead04aa0456773561b9b06f5926ce06 100644
--- a/examples/json_save.ipynb
+++ b/examples/json_save.ipynb
@@ -30,7 +30,7 @@
"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",
+ "from splearn import Spectral, Automaton, Serializer\n",
"train_file = '3.pautomac_light.train'\n",
"data = load_data_sample(adr=get_dataset_path(train_file))\n",
"sp = Spectral()\n",
@@ -39,26 +39,296 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Automaton.write(sp.automaton, train_file + \".json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "str1 = Serializer.data_to_json(sp.automaton)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "A = Serializer.json_to_data(str1) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "str2 = Serializer.data_to_json(A)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "str1 == str2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
- "ename": "AttributeError",
- "evalue": "'dict' object has no attribute 'iteritems'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-2-2625a21006a9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautomaton\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"test.json\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m~/Codes/scikit-splearn/splearn/automaton.py\u001b[0m in \u001b[0;36mwrite_json\u001b[0;34m(self, jsonFileName)\u001b[0m\n\u001b[1;32m 695\u001b[0m \"transitions\":self.transitions, \"type\":self.type}\n\u001b[1;32m 696\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjsonFileName\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0moutfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 697\u001b[0;31m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSerializer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_to_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 699\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjsonFileName\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/Codes/scikit-splearn/splearn/serializer.py\u001b[0m in \u001b[0;36mdata_to_json\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdata_to_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__serialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mjson_to_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/Codes/scikit-splearn/splearn/serializer.py\u001b[0m in \u001b[0;36m__serialize\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__serialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"py/dict\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__serialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__serialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'iteritems'"
+ "data": {
+ "text/plain": [
+ "'{\"automaton\": {\"transitions\": [{\"numpy.ndarray\": {\"values\": [[0.045121209595118526, -0.24038969827843812, 0.34944999592135073, -0.2811680730534614, -0.2140252337749723], [0.0692580056243754, -0.30062293462828926, 0.20641375368520323, -0.14960814319757162, -0.5580573163749147], [0.029801151921765866, -0.13866480809160275, 0.18362212572805375, -0.20969545230657946, -0.14481622025561033], [0.005699344003197941, -0.023385825120200612, -0.0660066537398175, 0.10749935271465784, -0.1510365460415996], [-0.020086551931478853, 0.09026347555230403, -0.005525585655539282, -0.03135531709030631, 0.24329022420477361]], \"dtype\": \"float64\"}}, {\"numpy.ndarray\": {\"values\": [[0.07744772079170577, 0.09007073705761888, -0.3047220063293004, 0.2767624549859125, 0.2028939603062788], [-0.0990298048367086, -0.08061846818727912, 0.2585317069225045, -0.12086330214608965, -0.11085207725068019], [-0.061710792028537555, -0.06244151779954693, 0.12007654564862041, 0.0025063746277926833, -0.15679674731455712], [-0.002736973749965318, -0.009005721984277385, -0.0004600329590919212, -0.008550426472005884, -0.05375464678968064], [0.030987327588710818, 0.03972680066723204, -0.049971133509102365, 0.0035769411874977224, 0.14182576205856343]], \"dtype\": \"float64\"}}, {\"numpy.ndarray\": {\"values\": [[-0.06791915236220329, -0.1135793765908807, 0.3795539260405443, -0.21784979894047046, -0.22977695089937855], [0.11596642335411368, 0.14914956804629215, -0.1335750837668692, -0.008916063072030857, 0.3484153673774847], [0.011730817547426591, 0.01927380053195553, 0.04142658345867102, -0.03534658856098166, 0.02316491010895659], [0.0073289110755416255, 0.005536509132796289, -0.02245608295066666, 0.03611543477693135, -0.03851433900140641], [-0.010589894686551481, -0.010626616553723545, -0.0005431056456618302, -0.02556747670016023, 0.04984888818929077]], \"dtype\": \"float64\"}}, {\"numpy.ndarray\": {\"values\": [[0.0727621142778044, -0.01571955768557908, 0.07428592814589835, -0.10369861539250237, 0.024753473688334762], [-0.05607105449779192, -0.08896207276035564, 0.2763822539752204, -0.23711255828386232, 0.07372294122304948], [-0.007391294007753285, -0.04874179796387578, -0.6291239733858517, 0.4681627652157708, 0.09251699239093028], [-0.007110224931878148, -0.056233177358980875, -0.3660665856762066, -0.013297798115226096, 0.649103317749269], [0.0023355150085570832, -0.021561151264484168, 0.09096243479437624, -0.3843882349306193, 0.6616477207948629]], \"dtype\": \"float64\"}}], \"type\": \"classic\", \"nbL\": 4, \"nbS\": 5, \"initial\": {\"numpy.ndarray\": {\"values\": [-0.0004934419970497477, 0.003063469710791532, -0.0440739320155803, -0.10777702616547354, -0.0866391379316936], \"dtype\": \"float64\"}}, \"final\": {\"numpy.ndarray\": {\"values\": [0.07757136847945034, -0.02422029400314446, -0.4468125366321277, 0.6277320840897538, -0.554674433356226], \"dtype\": \"float64\"}}}}'"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "str1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"automaton\": {\"transitions\": [{\"numpy.ndarray\": {\"values\": [[0.045121209595118526, -0.24038969827843812, 0.34944999592135073, -0.2811680730534614, -0.2140252337749723], [0.0692580056243754, -0.30062293462828926, 0.20641375368520323, -0.14960814319757162, -0.5580573163749147], [0.029801151921765866, -0.13866480809160275, 0.18362212572805375, -0.20969545230657946, -0.14481622025561033], [0.005699344003197941, -0.023385825120200612, -0.0660066537398175, 0.10749935271465784, -0.1510365460415996], [-0.020086551931478853, 0.09026347555230403, -0.005525585655539282, -0.03135531709030631, 0.24329022420477361]], \"dtype\": \"float64\"}}, {\"numpy.ndarray\": {\"values\": [[0.07744772079170577, 0.09007073705761888, -0.3047220063293004, 0.2767624549859125, 0.2028939603062788], [-0.0990298048367086, -0.08061846818727912, 0.2585317069225045, -0.12086330214608965, -0.11085207725068019], [-0.061710792028537555, -0.06244151779954693, 0.12007654564862041, 0.0025063746277926833, -0.15679674731455712], [-0.002736973749965318, -0.009005721984277385, -0.0004600329590919212, -0.008550426472005884, -0.05375464678968064], [0.030987327588710818, 0.03972680066723204, -0.049971133509102365, 0.0035769411874977224, 0.14182576205856343]], \"dtype\": \"float64\"}}, {\"numpy.ndarray\": {\"values\": [[-0.06791915236220329, -0.1135793765908807, 0.3795539260405443, -0.21784979894047046, -0.22977695089937855], [0.11596642335411368, 0.14914956804629215, -0.1335750837668692, -0.008916063072030857, 0.3484153673774847], [0.011730817547426591, 0.01927380053195553, 0.04142658345867102, -0.03534658856098166, 0.02316491010895659], [0.0073289110755416255, 0.005536509132796289, -0.02245608295066666, 0.03611543477693135, -0.03851433900140641], [-0.010589894686551481, -0.010626616553723545, -0.0005431056456618302, -0.02556747670016023, 0.04984888818929077]], \"dtype\": \"float64\"}}, {\"numpy.ndarray\": {\"values\": [[0.0727621142778044, -0.01571955768557908, 0.07428592814589835, -0.10369861539250237, 0.024753473688334762], [-0.05607105449779192, -0.08896207276035564, 0.2763822539752204, -0.23711255828386232, 0.07372294122304948], [-0.007391294007753285, -0.04874179796387578, -0.6291239733858517, 0.4681627652157708, 0.09251699239093028], [-0.007110224931878148, -0.056233177358980875, -0.3660665856762066, -0.013297798115226096, 0.649103317749269], [0.0023355150085570832, -0.021561151264484168, 0.09096243479437624, -0.3843882349306193, 0.6616477207948629]], \"dtype\": \"float64\"}}], \"type\": \"classic\", \"nbL\": 4, \"nbS\": 5, \"initial\": {\"numpy.ndarray\": {\"values\": [-0.0004934419970497477, 0.003063469710791532, -0.0440739320155803, -0.10777702616547354, -0.0866391379316936], \"dtype\": \"float64\"}}, \"final\": {\"numpy.ndarray\": {\"values\": [0.07757136847945034, -0.02422029400314446, -0.4468125366321277, 0.6277320840897538, -0.554674433356226], \"dtype\": \"float64\"}}}}'"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "str2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "str3 = Serializer.data_to_yaml(sp.automaton)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "automaton:\n",
+ " final:\n",
+ " numpy.ndarray:\n",
+ " dtype: float64\n",
+ " values: [0.07757136847945034, -0.02422029400314446, -0.4468125366321277, 0.6277320840897538,\n",
+ " -0.554674433356226]\n",
+ " initial:\n",
+ " numpy.ndarray:\n",
+ " dtype: float64\n",
+ " values: [-0.0004934419970497477, 0.003063469710791532, -0.0440739320155803,\n",
+ " -0.10777702616547354, -0.0866391379316936]\n",
+ " nbL: 4\n",
+ " nbS: 5\n",
+ " transitions:\n",
+ " - numpy.ndarray:\n",
+ " dtype: float64\n",
+ " values:\n",
+ " - [0.045121209595118526, -0.24038969827843812, 0.34944999592135073, -0.2811680730534614,\n",
+ " -0.2140252337749723]\n",
+ " - [0.0692580056243754, -0.30062293462828926, 0.20641375368520323, -0.14960814319757162,\n",
+ " -0.5580573163749147]\n",
+ " - [0.029801151921765866, -0.13866480809160275, 0.18362212572805375, -0.20969545230657946,\n",
+ " -0.14481622025561033]\n",
+ " - [0.005699344003197941, -0.023385825120200612, -0.0660066537398175, 0.10749935271465784,\n",
+ " -0.1510365460415996]\n",
+ " - [-0.020086551931478853, 0.09026347555230403, -0.005525585655539282, -0.03135531709030631,\n",
+ " 0.24329022420477361]\n",
+ " - numpy.ndarray:\n",
+ " dtype: float64\n",
+ " values:\n",
+ " - [0.07744772079170577, 0.09007073705761888, -0.3047220063293004, 0.2767624549859125,\n",
+ " 0.2028939603062788]\n",
+ " - [-0.0990298048367086, -0.08061846818727912, 0.2585317069225045, -0.12086330214608965,\n",
+ " -0.11085207725068019]\n",
+ " - [-0.061710792028537555, -0.06244151779954693, 0.12007654564862041, 0.0025063746277926833,\n",
+ " -0.15679674731455712]\n",
+ " - [-0.002736973749965318, -0.009005721984277385, -0.0004600329590919212, -0.008550426472005884,\n",
+ " -0.05375464678968064]\n",
+ " - [0.030987327588710818, 0.03972680066723204, -0.049971133509102365, 0.0035769411874977224,\n",
+ " 0.14182576205856343]\n",
+ " - numpy.ndarray:\n",
+ " dtype: float64\n",
+ " values:\n",
+ " - [-0.06791915236220329, -0.1135793765908807, 0.3795539260405443, -0.21784979894047046,\n",
+ " -0.22977695089937855]\n",
+ " - [0.11596642335411368, 0.14914956804629215, -0.1335750837668692, -0.008916063072030857,\n",
+ " 0.3484153673774847]\n",
+ " - [0.011730817547426591, 0.01927380053195553, 0.04142658345867102, -0.03534658856098166,\n",
+ " 0.02316491010895659]\n",
+ " - [0.0073289110755416255, 0.005536509132796289, -0.02245608295066666, 0.03611543477693135,\n",
+ " -0.03851433900140641]\n",
+ " - [-0.010589894686551481, -0.010626616553723545, -0.0005431056456618302, -0.02556747670016023,\n",
+ " 0.04984888818929077]\n",
+ " - numpy.ndarray:\n",
+ " dtype: float64\n",
+ " values:\n",
+ " - [0.0727621142778044, -0.01571955768557908, 0.07428592814589835, -0.10369861539250237,\n",
+ " 0.024753473688334762]\n",
+ " - [-0.05607105449779192, -0.08896207276035564, 0.2763822539752204, -0.23711255828386232,\n",
+ " 0.07372294122304948]\n",
+ " - [-0.007391294007753285, -0.04874179796387578, -0.6291239733858517, 0.4681627652157708,\n",
+ " 0.09251699239093028]\n",
+ " - [-0.007110224931878148, -0.056233177358980875, -0.3660665856762066, -0.013297798115226096,\n",
+ " 0.649103317749269]\n",
+ " - [0.0023355150085570832, -0.021561151264484168, 0.09096243479437624, -0.3843882349306193,\n",
+ " 0.6616477207948629]\n",
+ " type: classic\n",
+ "\n"
]
}
],
"source": [
- "sp.automaton.write_json(\"test.json\")"
+ "print(str3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Ajs = Automaton.read(train_file + \".json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "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": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Ajs.transitions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Automaton.write(sp.automaton, train_file + \".yaml\", \"yaml\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Ayl = Automaton.read(train_file + \".yaml\", \"yaml\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "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": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Ayl.transitions"
]
},
{
@@ -85,7 +355,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.3"
+ "version": "3.5.2"
}
},
"nbformat": 4,
diff --git a/examples/json_save.py b/examples/json_save.py
deleted file mode 100644
index ae9eea10edca3e41801866d7405a8829c198497f..0000000000000000000000000000000000000000
--- a/examples/json_save.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from splearn.datasets.base import load_data_sample
-from splearn.tests.datasets.get_dataset_path import get_dataset_path
-from splearn import Spectral
-from splearn.automaton import Automaton
-train_file = '3.pautomac_light.train'
-data = load_data_sample(adr=get_dataset_path(train_file))
-sp = Spectral()
-sp.fit(X=data.data)
-
-sp.automaton.write_json("test.json")
-A = Automaton(1,1,[]).read_json("test.json")
-print(A)
-
diff --git a/examples/simple_automata.json b/examples/simple_automata.json
new file mode 100644
index 0000000000000000000000000000000000000000..ab271a879ea057c6064636ba019001158b41a863
--- /dev/null
+++ b/examples/simple_automata.json
@@ -0,0 +1 @@
+{"automaton": {"final": {"numpy.ndarray": {"values": [0.15, 0.9], "dtype": "float64"}}, "initial": {"numpy.ndarray": {"values": [0.5, 0.8], "dtype": "float64"}}, "transitions": [{"numpy.ndarray": {"values": [[0.5, 0.15], [0.9, 0]], "dtype": "float64"}}, {"numpy.ndarray": {"values": [[0, 0], [0, -0.15]], "dtype": "float64"}}], "type": "classic", "nbS": 2, "nbL": 2}}
diff --git a/examples/simple_automata.json.gv b/examples/simple_automata.json.gv
new file mode 100644
index 0000000000000000000000000000000000000000..0fd62e2d59b54c471ed4736cd2b9456c714a1897
--- /dev/null
+++ b/examples/simple_automata.json.gv
@@ -0,0 +1,15 @@
+//Simple Automata
+digraph {
+ 0 [label="0
+______
+> 0.50
+0.15 >"]
+ 1 [label="1
+______
+> 0.80
+0.90 >"]
+ 0 -> 0 [label="0:0.50"]
+ 0 -> 1 [label="0:0.15"]
+ 1 -> 0 [label="0:0.90"]
+ 1 -> 1 [label="1:-0.15"]
+}
diff --git a/examples/simple_automata.json.gv.pdf b/examples/simple_automata.json.gv.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..bcedcde8006bcfa94c68c2c82199bd53b985503d
Binary files /dev/null and b/examples/simple_automata.json.gv.pdf differ
diff --git a/examples/simple_automata.yaml b/examples/simple_automata.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b85b6aec2fdc2ffe36ac8799948651b8c8c456ef
--- /dev/null
+++ b/examples/simple_automata.yaml
@@ -0,0 +1,23 @@
+automaton:
+ final:
+ numpy.ndarray:
+ dtype: float64
+ values: [0.15, 0.9]
+ initial:
+ numpy.ndarray:
+ dtype: float64
+ values: [0.5, 0.8]
+ nbL: 2
+ nbS: 2
+ transitions:
+ - numpy.ndarray:
+ dtype: float64
+ values:
+ - [0.5, 0.15]
+ - [0.9, 0]
+ - numpy.ndarray:
+ dtype: float64
+ values:
+ - [0, 0]
+ - [0, -0.15]
+ type: classic
diff --git a/examples/simple_automata.yaml.gv b/examples/simple_automata.yaml.gv
new file mode 100644
index 0000000000000000000000000000000000000000..0fd62e2d59b54c471ed4736cd2b9456c714a1897
--- /dev/null
+++ b/examples/simple_automata.yaml.gv
@@ -0,0 +1,15 @@
+//Simple Automata
+digraph {
+ 0 [label="0
+______
+> 0.50
+0.15 >"]
+ 1 [label="1
+______
+> 0.80
+0.90 >"]
+ 0 -> 0 [label="0:0.50"]
+ 0 -> 1 [label="0:0.15"]
+ 1 -> 0 [label="0:0.90"]
+ 1 -> 1 [label="1:-0.15"]
+}
diff --git a/examples/simple_automata.yaml.gv.pdf b/examples/simple_automata.yaml.gv.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..bcedcde8006bcfa94c68c2c82199bd53b985503d
Binary files /dev/null and b/examples/simple_automata.yaml.gv.pdf differ
diff --git a/examples/test.json b/examples/test.json
deleted file mode 100644
index e4d8477590e688c7f9a2ece37a8c5331f3d3d963..0000000000000000000000000000000000000000
--- a/examples/test.json
+++ /dev/null
@@ -1 +0,0 @@
-{"nbL": 4, "nbS": 5, "initial": {"py/numpy.ndarray": {"values": [-0.0004934419970497512, 0.0030634697107912346, -0.044073932015580415, -0.1077770261654714, -0.0866391379316952], "dtype": "float64"}}, "final": {"py/numpy.ndarray": {"values": [0.07757136847945045, -0.024220294003132026, -0.4468125366321221, 0.627732084089759, -0.554674433356224], "dtype": "float64"}}, "transitions": [{"py/numpy.ndarray": {"values": [[0.04512120959511772, -0.24038969827844062, 0.34944999592135334, -0.2811680730534579, -0.21402523377497645], [0.0692580056243761, -0.30062293462829204, 0.20641375368520157, -0.14960814319756124, -0.5580573163749153], [0.02980115192176571, -0.13866480809160409, 0.18362212572805459, -0.20969545230657607, -0.14481622025561292], [0.005699344003198349, -0.023385825120201414, -0.06600665373981851, 0.10749935271466007, -0.15103654604159977], [-0.02008655193147911, 0.09026347555230492, -0.005525585655539262, -0.031355317090308935, 0.2432902242047721]], "dtype": "float64"}}, {"py/numpy.ndarray": {"values": [[0.0774477207917058, 0.09007073705762021, -0.3047220063293013, 0.2767624549859105, 0.20289396030628148], [-0.09902980483670844, -0.08061846818727973, 0.25853170692250554, -0.12086330214608881, -0.11085207725068251], [-0.061710792028537534, -0.06244151779954751, 0.12007654564862075, 0.0025063746277943564, -0.1567967473145572], [-0.002736973749965403, -0.009005721984277787, -0.00046003295909181354, -0.008550426472005344, -0.053754646789681754], [0.030987327588710728, 0.03972680066723246, -0.04997113350910248, 0.0035769411874962344, 0.1418257620585633]], "dtype": "float64"}}, {"py/numpy.ndarray": {"values": [[-0.06791915236220235, -0.11357937659088102, 0.37955392604054394, -0.21784979894046635, -0.22977695089938127], [0.11596642335411328, 0.14914956804629287, -0.13357508376686902, -0.008916063072034974, 0.3484153673774836], [0.011730817547426673, 0.019273800531955612, 0.0414265834586712, -0.035346588560982, 0.02316491010895583], [0.007328911075541707, 0.005536509132796312, -0.022456082950666856, 0.03611543477693187, -0.038514339001406585], [-0.010589894686551544, -0.010626616553723532, -0.000543105645661794, -0.025567476700160314, 0.04984888818929034]], "dtype": "float64"}}, {"py/numpy.ndarray": {"values": [[0.07276211427780357, -0.0157195576855797, 0.07428592814590385, -0.10369861539249735, 0.024753473688328077], [-0.05607105449779142, -0.08896207276035666, 0.27638225397521243, -0.2371125582838589, 0.07372294122306285], [-0.007391294007753122, -0.048741797963875705, -0.6291239733858526, 0.46816276521577677, 0.09251699239093385], [-0.007110224931878467, -0.05623317735898056, -0.36606658567620365, -0.013297798115225407, 0.6491033177492604], [0.002335515008556511, -0.021561151264484414, 0.09096243479437888, -0.38438823493062646, 0.6616477207948602]], "dtype": "float64"}}], "type": "classic"}
\ No newline at end of file
diff --git a/splearn/__init__.py b/splearn/__init__.py
index 88d3dc0f47225850a99aff6e86da8fb109219213..ea4eddc4722c984de23538a502d30a24a91b5eb4 100644
--- a/splearn/__init__.py
+++ b/splearn/__init__.py
@@ -1,5 +1,5 @@
from splearn.automaton import Automaton
-#from splearn.spectral import Learning
+from splearn.serializer import Serializer
from splearn.spectral import Spectral
from splearn.hankel import Hankel
__version__ = "1.1.0"
diff --git a/splearn/automaton.py b/splearn/automaton.py
index ad971aa8ce7ad36544fce40f99f9890eb5aba542..35e48e36c68df2d4a63298b13c9502ad47bf5431 100644
--- a/splearn/automaton.py
+++ b/splearn/automaton.py
@@ -3,7 +3,7 @@
"""
import numpy as np
-from splearn.serializer import Serializer
+
class Automaton(object):
""" Define an automaton with parameters
@@ -643,9 +643,9 @@ class Automaton(object):
>>> 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.
@@ -688,25 +688,48 @@ class Automaton(object):
out += label + "\"]\n"
out += "}\n"
return out
-
- def write_json(self, jsonFileName):
- data = {"nbL":self.nbL, "nbS":self.nbS, "initial":self.initial, "final":self.final,
- "transitions":self.transitions, "type":self.type}
- data_str = Serializer().data_to_json(data)
- with open(jsonFileName, 'w') as outfile:
+
+ @staticmethod
+ def write(automaton_in, filename, format='json'):
+ """ write input automaton into a file with the given format.
+
+ - Input:
+
+ :param Automaton automaton_in: automaton to write into the file
+ :param str filename: the name of the file. If it does not exist,
+ the file is created.
+ :param str format: 'json' or yaml'
+ """
+ from splearn.serializer import Serializer
+ if format == 'json':
+ data_str = Serializer.data_to_json(automaton_in)
+ elif format == 'yaml':
+ data_str = Serializer.data_to_yaml(automaton_in)
+ else:
+ raise ValueError("Invalid input format. Should be \"json\" or \"yaml\"")
+ with open(filename, 'w') as outfile:
outfile.write(data_str)
-
- def read_json(self, jsonFileName):
- with open(jsonFileName) as infile:
+
+ @staticmethod
+ def read(filename, format='json'):
+ """ return an Automaton build with attributes read from a file
+
+ - Input:
+
+ :param str filename: the name of the input file.
+ :param str format: 'json' or yaml'
+
+ - Output:
+
+ :returns: the output automaton
+ :rtype: Automaton
+ """
+ from splearn.serializer import Serializer
+ with open(filename) as infile:
datastr = infile.read()
- data = Serializer().json_to_data(datastr)
- print(data)
- keys = {"nbL", "nbS", "initial", "final", "transitions", "type"}
- if not keys.issubset(set(data.keys())):
- raise ValueError("The input json file should contain the following keys : \"" + '\", \"'.join(keys) + "\"")
- self = Automaton(nbL=data["nbL"], nbS=data["nbS"], initial=data["initial"], final=data["final"],
- transitions=data["transitions"], type=data["type"])
-
-
-
-
+ if format == 'json':
+ return Serializer.json_to_data(datastr)
+ if format == 'yaml':
+ return Serializer.yaml_to_data(datastr)
+ raise ValueError("Invalid input format. Should be \"json\" or \"yaml\"")
+
\ No newline at end of file
diff --git a/splearn/serializer.py b/splearn/serializer.py
index b8645acad9c26cf161bf865096cec76157284681..2ba5e90d7a6a2d201fe4ec2ad7b72af00ae54b2e 100644
--- a/splearn/serializer.py
+++ b/splearn/serializer.py
@@ -1,50 +1,157 @@
-'''
-Created on 6 févr. 2018
-
-@author: arrivault
-'''
+# -*- coding: utf-8 -*-
+"""This module contains the Serializer class
+"""
import numpy as np
-import json
+
+from splearn.automaton import Automaton
class Serializer(object):
- '''
- classdocs
- '''
-
- def __serialize(self, data):
+ """ Serializer is an helping object for data serialization
+ """
+
+ @staticmethod
+ def __serialize(data):
if data is None or isinstance(data, (bool, int, float, str)):
return data
if isinstance(data, list):
- return [self.__serialize(val) for val in data]
+ return [Serializer.__serialize(val) for val in data]
if isinstance(data, dict):
if all(isinstance(k, str) for k in data):
- return {k: self.__serialize(v) for k, v in data.items()}
- return {"py/dict": [[self.__serialize(k), self.__serialize(v)] for k, v in data.items()]}
+ return {k: Serializer.__serialize(v) for k, v in data.items()}
+ return {"dict": [[Serializer.__serialize(k), Serializer.__serialize(v)] for k, v in data.items()]}
if isinstance(data, tuple):
- return {"py/tuple": [self.__serialize(val) for val in data]}
+ return {"tuple": [Serializer.__serialize(val) for val in data]}
if isinstance(data, set):
- return {"py/set": [self.__serialize(val) for val in data]}
+ return {"set": [Serializer.__serialize(val) for val in data]}
if isinstance(data, np.ndarray):
- return {"py/numpy.ndarray": {
+ return {"numpy.ndarray": {
"values": data.tolist(),
"dtype": str(data.dtype)}}
- raise TypeError("Type %s not data-serializable" % type(data))
+ if isinstance(data, Automaton):
+ data_dict = {"nbL":data.nbL, "nbS":data.nbS, "initial":data.initial, "final":data.final,
+ "transitions":data.transitions, "type":data.type}
+ return {"automaton" : Serializer.__serialize(data_dict)}
+ raise TypeError("Type %s is not serializabled" % type(data))
- def __restore(self, dct):
- if "py/dict" in dct:
- return dict(dct["py/dict"])
- if "py/tuple" in dct:
- return tuple(dct["py/tuple"])
- if "py/set" in dct:
- return set(dct["py/set"])
- if "py/numpy.ndarray" in dct:
- data = dct["py/numpy.ndarray"]
+ @staticmethod
+ def __restore_json(data_str):
+ if "dict" in data_str:
+ return dict(data_str["dict"])
+ if "tuple" in data_str:
+ return tuple(data_str["tuple"])
+ if "set" in data_str:
+ return set(data_str["set"])
+ if "numpy.ndarray" in data_str:
+ data = data_str["numpy.ndarray"]
+ keys = {"values", "dtype"}
+ if not keys.issubset(set(data.keys())):
+ raise ValueError("The input data string (" + data_str +
+ ") should contain the following keys : \"" +
+ '\", \"'.join(keys) + "\"")
return np.array(data["values"], dtype=data["dtype"])
- return dct
+ if "automaton" in data_str:
+ data = Serializer.__restore_json(data_str["automaton"])
+ keys = {"nbL", "nbS", "initial", "final", "transitions", "type"}
+ if not keys.issubset(set(data.keys())):
+ raise ValueError("The input data string (" + data_str +
+ ") should contain the following keys : \"" +
+ '\", \"'.join(keys) + "\"")
+ return Automaton(nbL=data["nbL"], nbS=data["nbS"], initial=data["initial"], final=data["final"],
+ transitions=data["transitions"], type=data["type"])
+ return data_str
+
+ @staticmethod
+ def __restore_yaml(data_str):
+ if "dict" in data_str:
+ return dict(data_str["dict"])
+ if "tuple" in data_str:
+ return tuple(data_str["tuple"])
+ if "set" in data_str:
+ return set(data_str["set"])
+ if "numpy.ndarray" in data_str:
+ data = data_str["numpy.ndarray"]
+ keys = {"values", "dtype"}
+ if not keys.issubset(set(data.keys())):
+ raise ValueError("The input data string (" + data_str +
+ ") should contain the following keys : \"" +
+ '\", \"'.join(keys) + "\"")
+ return np.array(data["values"], dtype=data["dtype"])
+ if "automaton" in data_str:
+ data = Serializer.__restore_yaml(data_str["automaton"])
+ keys = {"nbL", "nbS", "initial", "final", "transitions", "type"}
+ if not keys.issubset(set(data.keys())):
+ raise ValueError("The input data string (" + data_str +
+ ") should contain the following keys : \"" +
+ '\", \"'.join(keys) + "\"")
+ return Automaton(nbL=data["nbL"], nbS=data["nbS"], initial=Serializer.__restore_yaml(data["initial"]),
+ final=Serializer.__restore_yaml(data["final"]),
+ transitions=[Serializer.__restore_yaml(k) for k in data["transitions"]],
+ type=data["type"])
+ return data_str
+
+ @staticmethod
+ def data_to_json(data):
+ """ return a string into json format that does contains the input data.
+
+ - Input:
+
+ :param data: data composed by any types that is serializabled
+
+ - Output:
+
+ :returns: the output string
+ :rtype: str
+ """
+
+ import json
+ return json.dumps(Serializer.__serialize(data))
+
+ @staticmethod
+ def json_to_data(json_data_str):
+ """ return a data from input json string.
+
+ - Input:
+
+ :param json_data_str: the json input string
+
+ - Output:
+
+ :returns: the data
+ :rtype: deduced form the json input string
+ """
+
+ import json
+ return json.loads(json_data_str, object_hook=Serializer.__restore_json)
- def data_to_json(self, data):
- return json.dumps(self.__serialize(data))
+ @staticmethod
+ def data_to_yaml(data):
+ """ return a string into yaml format that does contains the input data.
+
+ - Input:
+
+ :param data: data composed by any types that is serializabled
+
+ - Output:
+
+ :returns: the output string
+ :rtype: str
+ """
+ import yaml
+ return yaml.dump(Serializer.__serialize(data))
- def json_to_data(self, s):
- return json.loads(s, object_hook=self.__restore)
+ @staticmethod
+ def yaml_to_data(yaml_data_str):
+ """ return a data from input yaml string.
+
+ - Input:
+
+ :param yaml_data_str: the yaml input string
+
+ - Output:
+
+ :returns: the data
+ :rtype: deduced form the yaml input string
+ """
+ import yaml
+ return Serializer.__restore_yaml(yaml.load(yaml_data_str))
diff --git a/test.json b/test.json
deleted file mode 100644
index e4d8477590e688c7f9a2ece37a8c5331f3d3d963..0000000000000000000000000000000000000000
--- a/test.json
+++ /dev/null
@@ -1 +0,0 @@
-{"nbL": 4, "nbS": 5, "initial": {"py/numpy.ndarray": {"values": [-0.0004934419970497512, 0.0030634697107912346, -0.044073932015580415, -0.1077770261654714, -0.0866391379316952], "dtype": "float64"}}, "final": {"py/numpy.ndarray": {"values": [0.07757136847945045, -0.024220294003132026, -0.4468125366321221, 0.627732084089759, -0.554674433356224], "dtype": "float64"}}, "transitions": [{"py/numpy.ndarray": {"values": [[0.04512120959511772, -0.24038969827844062, 0.34944999592135334, -0.2811680730534579, -0.21402523377497645], [0.0692580056243761, -0.30062293462829204, 0.20641375368520157, -0.14960814319756124, -0.5580573163749153], [0.02980115192176571, -0.13866480809160409, 0.18362212572805459, -0.20969545230657607, -0.14481622025561292], [0.005699344003198349, -0.023385825120201414, -0.06600665373981851, 0.10749935271466007, -0.15103654604159977], [-0.02008655193147911, 0.09026347555230492, -0.005525585655539262, -0.031355317090308935, 0.2432902242047721]], "dtype": "float64"}}, {"py/numpy.ndarray": {"values": [[0.0774477207917058, 0.09007073705762021, -0.3047220063293013, 0.2767624549859105, 0.20289396030628148], [-0.09902980483670844, -0.08061846818727973, 0.25853170692250554, -0.12086330214608881, -0.11085207725068251], [-0.061710792028537534, -0.06244151779954751, 0.12007654564862075, 0.0025063746277943564, -0.1567967473145572], [-0.002736973749965403, -0.009005721984277787, -0.00046003295909181354, -0.008550426472005344, -0.053754646789681754], [0.030987327588710728, 0.03972680066723246, -0.04997113350910248, 0.0035769411874962344, 0.1418257620585633]], "dtype": "float64"}}, {"py/numpy.ndarray": {"values": [[-0.06791915236220235, -0.11357937659088102, 0.37955392604054394, -0.21784979894046635, -0.22977695089938127], [0.11596642335411328, 0.14914956804629287, -0.13357508376686902, -0.008916063072034974, 0.3484153673774836], [0.011730817547426673, 0.019273800531955612, 0.0414265834586712, -0.035346588560982, 0.02316491010895583], [0.007328911075541707, 0.005536509132796312, -0.022456082950666856, 0.03611543477693187, -0.038514339001406585], [-0.010589894686551544, -0.010626616553723532, -0.000543105645661794, -0.025567476700160314, 0.04984888818929034]], "dtype": "float64"}}, {"py/numpy.ndarray": {"values": [[0.07276211427780357, -0.0157195576855797, 0.07428592814590385, -0.10369861539249735, 0.024753473688328077], [-0.05607105449779142, -0.08896207276035666, 0.27638225397521243, -0.2371125582838589, 0.07372294122306285], [-0.007391294007753122, -0.048741797963875705, -0.6291239733858526, 0.46816276521577677, 0.09251699239093385], [-0.007110224931878467, -0.05623317735898056, -0.36606658567620365, -0.013297798115225407, 0.6491033177492604], [0.002335515008556511, -0.021561151264484414, 0.09096243479437888, -0.38438823493062646, 0.6616477207948602]], "dtype": "float64"}}], "type": "classic"}
\ No newline at end of file