Adding io functions for Automata

a704c1a1 · Denis Arrivault · 71219463 · a704c1a1 · a704c1a1 · a704c1a1
Commit a704c1a1 authored Feb 7, 2018 by Denis Arrivault
--- a/COPYING
+++ b/COPYING
 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

--- a/copyrightstamp.txt
+++ b/copyrightstamp.txt
 ######### 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>
+* François Denis <francois.denis_AT_lis-lab.fr>
-* Rémi Eyraud <remy.eyraud_AT_lif.univ-mrs.fr>
+* Rémi Eyraud <remi.eyraud_AT_lis-lab.fr>
-* Denis Arrivault <contact.dev_AT_lif.univ-mrs.fr>
+* Denis Arrivault <contact.dev_AT_lis-lab.fr>
 * Dominique Benielli <dominique.benielli_AT_univ-amu.fr>
 Description:

--- a/examples/3.pautomac_light.train.json
+++ b/examples/3.pautomac_light.train.json
+{"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
--- a/examples/3.pautomac_light.train.yaml
+++ b/examples/3.pautomac_light.train.yaml
+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
--- a/examples/automaton_io.py
+++ b/examples/automaton_io.py
+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) 
--- 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",
+     "data": {
-     "evalue": "'dict' object has no attribute 'iteritems'",
+      "text/plain": [
-     "output_type": "error",
+       "'{\"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\"}}}}'"
-     "traceback": [
+      ]
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+     },
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+     "execution_count": 8,
-      "\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",
+     "metadata": {},
-      "\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",
+     "output_type": "execute_result"
-      "\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'"
+   "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": [
+    "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": [
-    "sp.automaton.write_json(\"test.json\")"
+    "Ayl.transitions"
   ]
  },
  {
@@ -85,7 +355,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.5.2"
  }
 },
 "nbformat": 4,

 %% Cell type:code id: tags:
 ``` python
 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 import Spectral, Automaton, Serializer
 train_file = '3.pautomac_light.train'
 data = load_data_sample(adr=get_dataset_path(train_file))
 sp = Spectral()
 sp.fit(X=data.data)
 ```
 %% Output
    Start Hankel matrix computation
    End of Hankel matrix computation
    Start Building Automaton from Hankel matrix
    End of Automaton computation
    Spectral(lcolumns=7, lrows=7, mode_quiet=False, partial=True, rank=5,
         smooth_method='none', sparse=True, version='classic')
 %% Cell type:code id: tags:
 ``` python
-sp.automaton.write_json("test.json")
+Automaton.write(sp.automaton, train_file + ".json")
+```
+%% Cell type:code id: tags:
+``` python
+str1 = Serializer.data_to_json(sp.automaton)
+```
+%% Cell type:code id: tags:
+``` python
+A = Serializer.json_to_data(str1)
+```
+%% Cell type:code id: tags:
+``` python
+str2 = Serializer.data_to_json(A)
+```
+%% Cell type:code id: tags:
+``` python
+str1 == str2
+```
+%% Output
+    True
+%% Cell type:code id: tags:
+``` python
+str1
+```
+%% Output
+    '{"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"}}}}'
+%% Cell type:code id: tags:
+``` python
+str2
+```
+%% Output
+    '{"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"}}}}'
+%% Cell type:code id: tags:
+``` python
+str3 = Serializer.data_to_yaml(sp.automaton)
+```
+%% Cell type:code id: tags:
+``` python
+print(str3)
+```
+%% Output
+    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
+%% Cell type:code id: tags:
+``` python
+Ajs = Automaton.read(train_file + ".json")
+```
+%% Cell type:code id: tags:
+``` python
+Ajs.transitions
+```
+%% Output
+    [array([[ 0.04512121, -0.2403897 ,  0.34945   , -0.28116807, -0.21402523],
+            [ 0.06925801, -0.30062293,  0.20641375, -0.14960814, -0.55805732],
+            [ 0.02980115, -0.13866481,  0.18362213, -0.20969545, -0.14481622],
+            [ 0.00569934, -0.02338583, -0.06600665,  0.10749935, -0.15103655],
+            [-0.02008655,  0.09026348, -0.00552559, -0.03135532,  0.24329022]]),
+     array([[ 0.07744772,  0.09007074, -0.30472201,  0.27676245,  0.20289396],
+            [-0.0990298 , -0.08061847,  0.25853171, -0.1208633 , -0.11085208],
+            [-0.06171079, -0.06244152,  0.12007655,  0.00250637, -0.15679675],
+            [-0.00273697, -0.00900572, -0.00046003, -0.00855043, -0.05375465],
+            [ 0.03098733,  0.0397268 , -0.04997113,  0.00357694,  0.14182576]]),
+     array([[-0.06791915, -0.11357938,  0.37955393, -0.2178498 , -0.22977695],
+            [ 0.11596642,  0.14914957, -0.13357508, -0.00891606,  0.34841537],
+            [ 0.01173082,  0.0192738 ,  0.04142658, -0.03534659,  0.02316491],
+            [ 0.00732891,  0.00553651, -0.02245608,  0.03611543, -0.03851434],
+            [-0.01058989, -0.01062662, -0.00054311, -0.02556748,  0.04984889]]),
+     array([[ 0.07276211, -0.01571956,  0.07428593, -0.10369862,  0.02475347],
+            [-0.05607105, -0.08896207,  0.27638225, -0.23711256,  0.07372294],
+            [-0.00739129, -0.0487418 , -0.62912397,  0.46816277,  0.09251699],
+            [-0.00711022, -0.05623318, -0.36606659, -0.0132978 ,  0.64910332],
+            [ 0.00233552, -0.02156115,  0.09096243, -0.38438823,  0.66164772]])]
+%% Cell type:code id: tags:
+``` python
+Automaton.write(sp.automaton, train_file + ".yaml", "yaml")
+```
+%% Cell type:code id: tags:
+``` python
+Ayl = Automaton.read(train_file + ".yaml", "yaml")
+```
+%% Cell type:code id: tags:
+``` python
+Ayl.transitions
 ```
 %% Output
-    ---------------------------------------------------------------------------
+    [array([[ 0.04512121, -0.2403897 ,  0.34945   , -0.28116807, -0.21402523],
-    AttributeError                            Traceback (most recent call last)
+            [ 0.06925801, -0.30062293,  0.20641375, -0.14960814, -0.55805732],
-    <ipython-input-2-2625a21006a9> in <module>()
+            [ 0.02980115, -0.13866481,  0.18362213, -0.20969545, -0.14481622],
-    ----> 1 sp.automaton.write_json("test.json")
+            [ 0.00569934, -0.02338583, -0.06600665,  0.10749935, -0.15103655],
+            [-0.02008655,  0.09026348, -0.00552559, -0.03135532,  0.24329022]]),
-    ~/Codes/scikit-splearn/splearn/automaton.py in write_json(self, jsonFileName)
+     array([[ 0.07744772,  0.09007074, -0.30472201,  0.27676245,  0.20289396],
-        695                 "transitions":self.transitions, "type":self.type}
+            [-0.0990298 , -0.08061847,  0.25853171, -0.1208633 , -0.11085208],
-        696         with open(jsonFileName, 'w') as outfile:
+            [-0.06171079, -0.06244152,  0.12007655,  0.00250637, -0.15679675],
-    --> 697             json.dump(Serializer().data_to_json(data), outfile)
+            [-0.00273697, -0.00900572, -0.00046003, -0.00855043, -0.05375465],
-        698
+            [ 0.03098733,  0.0397268 , -0.04997113,  0.00357694,  0.14182576]]),
-        699     def read_json(self, jsonFileName):
+     array([[-0.06791915, -0.11357938,  0.37955393, -0.2178498 , -0.22977695],
-    ~/Codes/scikit-splearn/splearn/serializer.py in data_to_json(self, data)
+            [ 0.11596642,  0.14914957, -0.13357508, -0.00891606,  0.34841537],
-         45
+            [ 0.01173082,  0.0192738 ,  0.04142658, -0.03534659,  0.02316491],
-         46     def data_to_json(self, data):
+            [ 0.00732891,  0.00553651, -0.02245608,  0.03611543, -0.03851434],
-    ---> 47         return json.dumps(self.__serialize(data))
+            [-0.01058989, -0.01062662, -0.00054311, -0.02556748,  0.04984889]]),
-         48
+     array([[ 0.07276211, -0.01571956,  0.07428593, -0.10369862,  0.02475347],
-         49     def json_to_data(self, s):
+            [-0.05607105, -0.08896207,  0.27638225, -0.23711256,  0.07372294],
-    ~/Codes/scikit-splearn/splearn/serializer.py in __serialize(self, data)
+            [-0.00739129, -0.0487418 , -0.62912397,  0.46816277,  0.09251699],
-         20         if isinstance(data, dict):
+            [-0.00711022, -0.05623318, -0.36606659, -0.0132978 ,  0.64910332],
-         21             if all(isinstance(k, str) for k in data):
+            [ 0.00233552, -0.02156115,  0.09096243, -0.38438823,  0.66164772]])]
-    ---> 22                 return {k: self.__serialize(v) for k, v in data.iteritems()}
-         23             return {"py/dict": [[self.__serialize(k), self.__serialize(v)] for k, v in data.iteritems()]}
-         24         if isinstance(data, tuple):
-    AttributeError: 'dict' object has no attribute 'iteritems'
 %% Cell type:code id: tags:
 ``` python
 ```

--- a/examples/json_save.py
+++ b/examples/json_save.py
-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)
--- a/examples/simple_automata.json
+++ b/examples/simple_automata.json
+{"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}}
--- a/examples/simple_automata.json.gv
+++ b/examples/simple_automata.json.gv
+//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"]
+}
--- a/examples/simple_automata.json.gv.pdf
+++ b/examples/simple_automata.json.gv.pdf
--- a/examples/simple_automata.yaml
+++ b/examples/simple_automata.yaml
+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
--- a/examples/simple_automata.yaml.gv
+++ b/examples/simple_automata.yaml.gv
+//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"]
+}
--- a/examples/simple_automata.yaml.gv.pdf
+++ b/examples/simple_automata.yaml.gv.pdf
--- a/examples/test.json
+++ b/examples/test.json
-{"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
--- a/splearn/__init__.py
+++ b/splearn/__init__.py
 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"
--- 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
@@ -689,24 +689,47 @@ class Automaton(object):
        out += "}\n"
        return out
-    def write_json(self, jsonFileName):
+    @staticmethod
-        data = {"nbL":self.nbL, "nbS":self.nbS, "initial":self.initial, "final":self.final, 
+    def write(automaton_in, filename, format='json'):
-                "transitions":self.transitions, "type":self.type}
+        """ write input automaton into a file with the given format.
-        data_str = Serializer().data_to_json(data)
-        with open(jsonFileName, 'w') as outfile:
+        - 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):
+    @staticmethod
-        with open(jsonFileName) as infile:
+    def read(filename, format='json'):
-            datastr = infile.read()
+        """ return an Automaton build with attributes read from a file
-        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"])
+        - 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()
+        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
--- a/splearn/serializer.py
+++ b/splearn/serializer.py
-'''
+# -*- coding: utf-8 -*-
-Created on 6 févr. 2018
+"""This module contains the Serializer class
+"""
-@author: arrivault
-'''
 import numpy as np
-import json
+from splearn.automaton import Automaton
 class Serializer(object):
-    '''
+    """ Serializer is an helping object for data serialization
-    classdocs
+    """
-    '''
-    def __serialize(self, data):
+    @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 {k: Serializer.__serialize(v) for k, v in data.items()}
-            return {"py/dict": [[self.__serialize(k), self.__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, 
-    def __restore(self, dct):
+                "transitions":data.transitions, "type":data.type}
-        if "py/dict" in dct:
+            return {"automaton" : Serializer.__serialize(data_dict)}
-            return dict(dct["py/dict"])
+        raise TypeError("Type %s is not serializabled" % type(data))
-        if "py/tuple" in dct:
-            return tuple(dct["py/tuple"])
+    @staticmethod
-        if "py/set" in dct:
+    def __restore_json(data_str):
-            return set(dct["py/set"])
+        if "dict" in data_str:
-        if "py/numpy.ndarray" in dct:
+            return dict(data_str["dict"])
-            data = dct["py/numpy.ndarray"]
+        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)
+    @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))
+    @staticmethod
+    def yaml_to_data(yaml_data_str):
+        """ return a data from input yaml string.
+        - Input:
+        :param yaml_data_str: the yaml input string
-    def data_to_json(self, data):
+        - Output:
-        return json.dumps(self.__serialize(data))
-    def json_to_data(self, s):
+        :returns: the data
-        return json.loads(s, object_hook=self.__restore)
+        :rtype: deduced form the yaml input string
+        """
+        import yaml
+        return Serializer.__restore_yaml(yaml.load(yaml_data_str))
--- a/test.json
+++ b/test.json
-{"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