diff --git a/examples/3.pautomac_light.train.json b/examples/3.pautomac_light.train.json
index e77f61549383993bfd88d1693caf1b8a49cd23f1..56415b8ee3cc099afc1dc377605708d8a08860b4 100644
--- a/examples/3.pautomac_light.train.json
+++ b/examples/3.pautomac_light.train.json
@@ -1 +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
+{"automaton": {"nbL": 4, "nbS": 5, "initial": {"numpy.ndarray": {"values": [-0.000493441997049692, 0.003063469710791502, -0.04407393201558057, -0.10777702616547158, -0.0866391379316951], "dtype": "float64"}}, "final": {"numpy.ndarray": {"values": [0.07757136847945678, -0.024220294003121035, -0.4468125366321232, 0.6277320840897611, -0.5546744333562244], "dtype": "float64"}}, "transitions": [{"numpy.ndarray": {"values": [[0.045121209595118054, -0.24038969827844267, 0.3494499959213531, -0.2811680730534573, -0.21402523377497668], [0.06925800562437727, -0.3006229346282947, 0.2064137536852026, -0.14960814319756102, -0.5580573163749218], [0.02980115192176601, -0.13866480809160522, 0.18362212572805475, -0.20969545230657657, -0.14481622025561255], [0.005699344003198416, -0.023385825120200706, -0.06600665373981858, 0.10749935271466031, -0.1510365460416008], [-0.020086551931479287, 0.09026347555230453, -0.005525585655539293, -0.031355317090309115, 0.2432902242047725]], "dtype": "float64"}}, {"numpy.ndarray": {"values": [[0.07744772079170498, 0.09007073705762163, -0.3047220063293005, 0.27676245498591084, 0.20289396030627946], [-0.09902980483670908, -0.08061846818728234, 0.2585317069225073, -0.12086330214608876, -0.11085207725068613], [-0.06171079202853737, -0.06244151779954791, 0.12007654564862096, 0.0025063746277944722, -0.1567967473145574], [-0.0027369737499654224, -0.009005721984277773, -0.0004600329590916909, -0.00855042647200538, -0.053754646789682024], [0.03098732758871066, 0.03972680066723251, -0.049971133509102664, 0.003576941187496189, 0.14182576205856365]], "dtype": "float64"}}, {"numpy.ndarray": {"values": [[-0.06791915236220136, -0.11357937659088249, 0.379553926040543, -0.21784979894046527, -0.229776950899381], [0.11596642335411327, 0.14914956804629298, -0.13357508376686894, -0.008916063072034729, 0.3484153673774827], [0.01173081754742677, 0.019273800531955328, 0.04142658345867112, -0.03534658856098203, 0.02316491010895624], [0.007328911075541722, 0.005536509132796604, -0.022456082950666895, 0.03611543477693201, -0.03851433900140671], [-0.010589894686551596, -0.010626616553723715, -0.0005431056456617233, -0.02556747670016042, 0.049848888189290286]], "dtype": "float64"}}, {"numpy.ndarray": {"values": [[0.07276211427780344, -0.015719557685580397, 0.07428592814590271, -0.10369861539249554, 0.02475347368832667], [-0.05607105449779084, -0.08896207276035853, 0.2763822539752058, -0.23711255828384722, 0.07372294122304912], [-0.007391294007754002, -0.048741797963871694, -0.6291239733858526, 0.46816276521577743, 0.09251699239092943], [-0.007110224931879211, -0.05623317735897968, -0.366066585676203, -0.013297798115225577, 0.649103317749257], [0.0023355150085563733, -0.02156115126448696, 0.09096243479437824, -0.3843882349306287, 0.6616477207948644]], "dtype": "float64"}}], "type": "classic"}}
\ No newline at end of file
diff --git a/examples/3.pautomac_light.train.yaml b/examples/3.pautomac_light.train.yaml
index 125acbb420b9067bb9ee4097e4ed5312dc268f64..c71fe8099efcf0c5e68836c5b24d6936c5a92cbe 100644
--- a/examples/3.pautomac_light.train.yaml
+++ b/examples/3.pautomac_light.train.yaml
@@ -2,66 +2,66 @@ automaton:
final:
numpy.ndarray:
dtype: float64
- values: [0.07757136847945034, -0.02422029400314446, -0.4468125366321277, 0.6277320840897538,
- -0.554674433356226]
+ values: [0.07757136847945678, -0.024220294003121035, -0.4468125366321232, 0.6277320840897611,
+ -0.5546744333562244]
initial:
numpy.ndarray:
dtype: float64
- values: [-0.0004934419970497477, 0.003063469710791532, -0.0440739320155803,
- -0.10777702616547354, -0.0866391379316936]
+ values: [-0.000493441997049692, 0.003063469710791502, -0.04407393201558057,
+ -0.10777702616547158, -0.0866391379316951]
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]
+ - [0.045121209595118054, -0.24038969827844267, 0.3494499959213531, -0.2811680730534573,
+ -0.21402523377497668]
+ - [0.06925800562437727, -0.3006229346282947, 0.2064137536852026, -0.14960814319756102,
+ -0.5580573163749218]
+ - [0.02980115192176601, -0.13866480809160522, 0.18362212572805475, -0.20969545230657657,
+ -0.14481622025561255]
+ - [0.005699344003198416, -0.023385825120200706, -0.06600665373981858, 0.10749935271466031,
+ -0.1510365460416008]
+ - [-0.020086551931479287, 0.09026347555230453, -0.005525585655539293, -0.031355317090309115,
+ 0.2432902242047725]
- 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]
+ - [0.07744772079170498, 0.09007073705762163, -0.3047220063293005, 0.27676245498591084,
+ 0.20289396030627946]
+ - [-0.09902980483670908, -0.08061846818728234, 0.2585317069225073, -0.12086330214608876,
+ -0.11085207725068613]
+ - [-0.06171079202853737, -0.06244151779954791, 0.12007654564862096, 0.0025063746277944722,
+ -0.1567967473145574]
+ - [-0.0027369737499654224, -0.009005721984277773, -0.0004600329590916909, -0.00855042647200538,
+ -0.053754646789682024]
+ - [0.03098732758871066, 0.03972680066723251, -0.049971133509102664, 0.003576941187496189,
+ 0.14182576205856365]
- 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]
+ - [-0.06791915236220136, -0.11357937659088249, 0.379553926040543, -0.21784979894046527,
+ -0.229776950899381]
+ - [0.11596642335411327, 0.14914956804629298, -0.13357508376686894, -0.008916063072034729,
+ 0.3484153673774827]
+ - [0.01173081754742677, 0.019273800531955328, 0.04142658345867112, -0.03534658856098203,
+ 0.02316491010895624]
+ - [0.007328911075541722, 0.005536509132796604, -0.022456082950666895, 0.03611543477693201,
+ -0.03851433900140671]
+ - [-0.010589894686551596, -0.010626616553723715, -0.0005431056456617233, -0.02556747670016042,
+ 0.049848888189290286]
- 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]
+ - [0.07276211427780344, -0.015719557685580397, 0.07428592814590271, -0.10369861539249554,
+ 0.02475347368832667]
+ - [-0.05607105449779084, -0.08896207276035853, 0.2763822539752058, -0.23711255828384722,
+ 0.07372294122304912]
+ - [-0.007391294007754002, -0.048741797963871694, -0.6291239733858526, 0.46816276521577743,
+ 0.09251699239092943]
+ - [-0.007110224931879211, -0.05623317735897968, -0.366066585676203, -0.013297798115225577,
+ 0.649103317749257]
+ - [0.0023355150085563733, -0.02156115126448696, 0.09096243479437824, -0.3843882349306287,
+ 0.6616477207948644]
type: classic
diff --git a/examples/automaton_io.py b/examples/automaton_io.py
index 59559e3970797bf427256a106a6a6fbfcf859998..66eb5db268543f69e72ef9d14c103308dec87699 100644
--- a/examples/automaton_io.py
+++ b/examples/automaton_io.py
@@ -1,11 +1,37 @@
from splearn.automaton import Automaton
from graphviz import Source
+from splearn.hankel import Hankel
-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")
+title = "Simple Example"
+input_file = 'simple_example'
+A = Automaton.SimpleExample()
+formats = ['json', 'yaml']
+words = [[], [0], [0, 0], [1], [1, 1], [0, 1, 0], [1, 0, 1]]
+d_str = "Value for word \"{:s}\" = {:.2f}"
+# read simple automata in each format and write hankel
+for f in formats:
+ #A = Automaton.read(input_file + "." + f, format=f)
+ Automaton.write(A, input_file + '.' + f, format = f)
+ dot = A.get_dot(title = title)
src = Source(dot)
- src.render(input_file + '.' + f + '.gv', view=True)
+ src.render(input_file + '.' + f + '.gv', view=False)
+ # Display some values on words
+ print("First Automata")
+ for w in words:
+ print(d_str.format(str(w), A.val(w)))
+ H = A.to_hankel([(), (0,), (1,)], [(), (0,), (1,)])
+ Hankel.write(H, input_file + "_hankel" + "." + f, format=f)
+
+# read hankel and generate Automata
+for f in formats:
+ H = Hankel.read(input_file + "_hankel" + "." + f, format = f)
+ A = H.to_automaton(2)
+ dot = A.get_dot(title = title)
+ src = Source(dot)
+ src.render(input_file + '-2.' + f + '.gv', view=False)
+ # Display some values on words
+ print("Second Automata")
+ for w in words:
+ print(d_str.format(str(w), A.val(w)))
+
diff --git a/examples/json_save.ipynb b/examples/json_save.ipynb
index 1af50f640ead04aa0456773561b9b06f5926ce06..d42284d42a8fee69e4d145156c91fb7cb05c0d68 100644
--- a/examples/json_save.ipynb
+++ b/examples/json_save.ipynb
@@ -39,7 +39,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -48,7 +48,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -57,7 +57,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -66,7 +66,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -75,7 +75,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -84,7 +84,7 @@
"True"
]
},
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -95,16 +95,16 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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},
- "execution_count": 8,
+ "execution_count": 7,
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@@ -115,16 +115,16 @@
},
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+ "execution_count": 8,
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+ "'{\"automaton\": {\"nbL\": 4, \"nbS\": 5, \"initial\": {\"numpy.ndarray\": {\"values\": [-0.000493441997049692, 0.003063469710791502, -0.04407393201558057, -0.10777702616547158, -0.0866391379316951], \"dtype\": \"float64\"}}, \"final\": {\"numpy.ndarray\": {\"values\": [0.07757136847945678, -0.024220294003121035, -0.4468125366321232, 0.6277320840897611, -0.5546744333562244], \"dtype\": \"float64\"}}, \"transitions\": [{\"numpy.ndarray\": {\"values\": [[0.045121209595118054, -0.24038969827844267, 0.3494499959213531, -0.2811680730534573, -0.21402523377497668], [0.06925800562437727, -0.3006229346282947, 0.2064137536852026, -0.14960814319756102, -0.5580573163749218], [0.02980115192176601, -0.13866480809160522, 0.18362212572805475, -0.20969545230657657, -0.14481622025561255], [0.005699344003198416, -0.023385825120200706, -0.06600665373981858, 0.10749935271466031, -0.1510365460416008], [-0.020086551931479287, 0.09026347555230453, -0.005525585655539293, -0.031355317090309115, 0.2432902242047725]], \"dtype\": \"float64\"}}, {\"numpy.ndarray\": {\"values\": [[0.07744772079170498, 0.09007073705762163, -0.3047220063293005, 0.27676245498591084, 0.20289396030627946], [-0.09902980483670908, -0.08061846818728234, 0.2585317069225073, -0.12086330214608876, -0.11085207725068613], [-0.06171079202853737, -0.06244151779954791, 0.12007654564862096, 0.0025063746277944722, -0.1567967473145574], [-0.0027369737499654224, -0.009005721984277773, -0.0004600329590916909, -0.00855042647200538, -0.053754646789682024], [0.03098732758871066, 0.03972680066723251, -0.049971133509102664, 0.003576941187496189, 0.14182576205856365]], \"dtype\": \"float64\"}}, {\"numpy.ndarray\": {\"values\": [[-0.06791915236220136, -0.11357937659088249, 0.379553926040543, -0.21784979894046527, -0.229776950899381], [0.11596642335411327, 0.14914956804629298, -0.13357508376686894, -0.008916063072034729, 0.3484153673774827], [0.01173081754742677, 0.019273800531955328, 0.04142658345867112, -0.03534658856098203, 0.02316491010895624], [0.007328911075541722, 0.005536509132796604, -0.022456082950666895, 0.03611543477693201, -0.03851433900140671], [-0.010589894686551596, -0.010626616553723715, -0.0005431056456617233, -0.02556747670016042, 0.049848888189290286]], \"dtype\": \"float64\"}}, {\"numpy.ndarray\": {\"values\": [[0.07276211427780344, -0.015719557685580397, 0.07428592814590271, -0.10369861539249554, 0.02475347368832667], [-0.05607105449779084, -0.08896207276035853, 0.2763822539752058, -0.23711255828384722, 0.07372294122304912], [-0.007391294007754002, -0.048741797963871694, -0.6291239733858526, 0.46816276521577743, 0.09251699239092943], [-0.007110224931879211, -0.05623317735897968, -0.366066585676203, -0.013297798115225577, 0.649103317749257], [0.0023355150085563733, -0.02156115126448696, 0.09096243479437824, -0.3843882349306287, 0.6616477207948644]], \"dtype\": \"float64\"}}], \"type\": \"classic\"}}'"
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},
- "execution_count": 9,
+ "execution_count": 8,
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@@ -135,7 +135,7 @@
},
{
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+ "execution_count": 9,
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"source": [
@@ -144,7 +144,7 @@
},
{
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- "execution_count": 11,
+ "execution_count": 10,
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{
@@ -155,68 +155,68 @@
" final:\n",
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" nbL: 4\n",
" nbS: 5\n",
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+ " - [0.007328911075541722, 0.005536509132796604, -0.022456082950666895, 0.03611543477693201,\n",
+ " -0.03851433900140671]\n",
+ " - [-0.010589894686551596, -0.010626616553723715, -0.0005431056456617233, -0.02556747670016042,\n",
+ " 0.049848888189290286]\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",
+ " - [0.07276211427780344, -0.015719557685580397, 0.07428592814590271, -0.10369861539249554,\n",
+ " 0.02475347368832667]\n",
+ " - [-0.05607105449779084, -0.08896207276035853, 0.2763822539752058, -0.23711255828384722,\n",
+ " 0.07372294122304912]\n",
+ " - [-0.007391294007754002, -0.048741797963871694, -0.6291239733858526, 0.46816276521577743,\n",
+ " 0.09251699239092943]\n",
+ " - [-0.007110224931879211, -0.05623317735897968, -0.366066585676203, -0.013297798115225577,\n",
+ " 0.649103317749257]\n",
+ " - [0.0023355150085563733, -0.02156115126448696, 0.09096243479437824, -0.3843882349306287,\n",
+ " 0.6616477207948644]\n",
" type: classic\n",
"\n"
]
@@ -228,7 +228,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -237,7 +237,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -265,7 +265,7 @@
" [ 0.00233552, -0.02156115, 0.09096243, -0.38438823, 0.66164772]])]"
]
},
- "execution_count": 14,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -276,7 +276,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -285,7 +285,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -294,7 +294,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -322,7 +322,7 @@
" [ 0.00233552, -0.02156115, 0.09096243, -0.38438823, 0.66164772]])]"
]
},
- "execution_count": 19,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -355,7 +355,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.6.3"
}
},
"nbformat": 4,
diff --git a/examples/simple_automata.json.gv.pdf b/examples/simple_automata.json.gv.pdf
index bcedcde8006bcfa94c68c2c82199bd53b985503d..9253c0b86d51eaa9a3f4014c4c4202bbf69c7aa4 100644
Binary files a/examples/simple_automata.json.gv.pdf and b/examples/simple_automata.json.gv.pdf differ
diff --git a/examples/simple_automata.yaml.gv.pdf b/examples/simple_automata.yaml.gv.pdf
index bcedcde8006bcfa94c68c2c82199bd53b985503d..9253c0b86d51eaa9a3f4014c4c4202bbf69c7aa4 100644
Binary files a/examples/simple_automata.yaml.gv.pdf and b/examples/simple_automata.yaml.gv.pdf differ
diff --git a/splearn/automaton.py b/splearn/automaton.py
index 35e48e36c68df2d4a63298b13c9502ad47bf5431..9a980d7b9f74a1b0fe10d43a8903a51a35dd36bf 100644
--- a/splearn/automaton.py
+++ b/splearn/automaton.py
@@ -304,8 +304,8 @@ class Automaton(object):
:rtype: Hankel
"""
from splearn.hankel import Hankel
- lhankels = self.BuildHankels(lrows, lcolumns)
- return Hankel(mode_quiet=mode_quiet, lhankels=lhankels, rank=self.nbS)
+ lhankel = self.BuildHankels(lrows, lcolumns)
+ return Hankel(mode_quiet=mode_quiet, lhankel=lhankel)
def mirror(self):
""" Compute the mirror automaton
diff --git a/splearn/hankel.py b/splearn/hankel.py
index 41d58ea824dc070c6ca485e8399eece504b3abc7..d91ff7220854c622f06f92c8a0e18594902c5431 100644
--- a/splearn/hankel.py
+++ b/splearn/hankel.py
@@ -48,22 +48,21 @@ class Hankel(object):
:param boolean mode_quiet: (default value = False) True for no
output message.
:param list of all Hankel matrices. At least one of the two parameters
- *sample_instance* or *lhankels* has to be not None. If *sample_instance* is given,
+ *sample_instance* or *lhankel* has to be not None. If *sample_instance* is given,
the **Hankel** instance is built directly from the sample dictionnary,
- else it is deduced from the *lhankels* list of matrices.
- :param int rank: the rank of the Hankel matrices. Required if **Hankel** is build from *lhankels*.
+ else it is deduced from the *lhankels* list of matrices.
"""
def __init__(
self, sample_instance=None,
lrows=[], lcolumns=[],
version="classic", partial=False,
- sparse=False, mode_quiet=False, lhankels=None, rank = -1):
+ sparse=False, mode_quiet=False, lhankel=None):
self.version = version
self.partial = partial
self.sparse = sparse
- self.buildFromSample = True
+ self.build_from_sample = True
if sample_instance is not None:
# Size of the alphabet
self.nbL = sample_instance.nbL
@@ -75,15 +74,12 @@ class Hankel(object):
fact=sample_instance.fact,
lrows=lrows, lcolumns=lcolumns,
mode_quiet=mode_quiet)
- elif lhankels is not None:
+ elif lhankel is not None:
# Size of the alphabet
- self.nbL = len(lhankels) - 1
- # Number of samples
- if rank == -1:
- raise ValueError("Building an Hankel instance from list of hankel matrices required a valid rank.")
- self.nbS = rank
- self.lhankel = lhankels
- self.buildFromSample = False
+ self.nbL = len(lhankel) - 1
+ self.lhankel = lhankel
+ self.build_from_sample = False
+ self._nbEx = -1
else:
raise ValueError("At least sample_instance or lhankel has to be not None.")
@@ -114,6 +110,18 @@ class Hankel(object):
raise ValueError("The number of examples should be " +
" an integer >= 0")
self._nbEx = nbEx
+
+ @property
+ def build_from_sample(self):
+ return self._build_from_sample
+
+ @build_from_sample.setter
+ def build_from_sample(self, val):
+ if val:
+ self._build_from_sample = True
+ else:
+ self._build_from_sample = False
+
def build(self, sample, pref, suff, fact, lrows, lcolumns, mode_quiet):
@@ -265,12 +273,12 @@ class Hankel(object):
return (drows, dcolumns)
- def to_automaton(self, rank, mode_quiet):
+ def to_automaton(self, rank, mode_quiet=False):
""" Return an automaton from the current Hankel matrix
- Input:
- :param int rank: the ranking number
+ :param int rank: the matrix rank
:param boolean mode_quiet: True for no output message.
- Output:
@@ -322,7 +330,7 @@ class Hankel(object):
A = Automaton(nbL=self.nbL, nbS=rank, initial=init, final=term,
transitions=trans, type=self.version)
- if self.buildFromSample:
+ if self.build_from_sample:
A.initial = A.initial / self.nbEx
if self.version == "prefix":
A = A.transformation(source="prefix", target="classic")
@@ -333,3 +341,47 @@ class Hankel(object):
if not mode_quiet:
print ("End of Automaton computation")
return A
+
+ @staticmethod
+ def write(hankel_in, filename, format='json'):
+ """ write input hankel into a file with the given format.
+
+ - Input:
+
+ :param Hankel hankel_in: hankel 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(hankel_in)
+ elif format == 'yaml':
+ data_str = Serializer.data_to_yaml(hankel_in)
+ else:
+ raise ValueError("Invalid input format. Should be \"json\" or \"yaml\"")
+ with open(filename, 'w') as outfile:
+ outfile.write(data_str)
+
+ @staticmethod
+ def read(filename, format='json'):
+ """ return a Hankel 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 hankel
+ :rtype: Hankel
+ """
+ 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\"")
diff --git a/splearn/serializer.py b/splearn/serializer.py
index 2ba5e90d7a6a2d201fe4ec2ad7b72af00ae54b2e..5109cdf3fc685b268719ad38f6db69affe3282b0 100644
--- a/splearn/serializer.py
+++ b/splearn/serializer.py
@@ -5,6 +5,8 @@
import numpy as np
from splearn.automaton import Automaton
+from splearn.hankel import Hankel
+from numpy.f2py.common_rules import findcommonblocks
class Serializer(object):
""" Serializer is an helping object for data serialization
@@ -32,6 +34,11 @@ class Serializer(object):
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)}
+ if isinstance(data, Hankel):
+ data_dict = {"nbL":data.nbL, "lhankel" : data.lhankel, "version" : data.version,
+ "partial" : data.partial, "sparse" : data.sparse,
+ "build_from_sample" : data.build_from_sample, "ndEx" : data.nbEx}
+ return {"hankel" : Serializer.__serialize(data_dict)}
raise TypeError("Type %s is not serializabled" % type(data))
@staticmethod
@@ -59,6 +66,21 @@ class Serializer(object):
'\", \"'.join(keys) + "\"")
return Automaton(nbL=data["nbL"], nbS=data["nbS"], initial=data["initial"], final=data["final"],
transitions=data["transitions"], type=data["type"])
+ if "hankel" in data_str:
+ data = Serializer.__restore_json(data_str["hankel"])
+ keys = {"nbL", "lhankel", "version", "partial", "sparse", "build_from_sample", "ndEx"}
+ if not keys.issubset(set(data.keys())):
+ raise ValueError("The input data string (" + data_str +
+ ") should contain the following keys : \"" +
+ '\", \"'.join(keys) + "\"")
+ H = Hankel(version=data["version"], partial=data["partial"], sparse=data["sparse"],
+ lhankel = data["lhankel"])
+ if data["build_from_sample"]:
+ H.build_from_sample = True
+ H.nbL = data["nbL"]
+ H.nbEx = data["nbEx"]
+ return H
+
return data_str
@staticmethod
@@ -88,6 +110,20 @@ class Serializer(object):
final=Serializer.__restore_yaml(data["final"]),
transitions=[Serializer.__restore_yaml(k) for k in data["transitions"]],
type=data["type"])
+ if "hankel" in data_str:
+ data = Serializer.__restore_json(data_str["hankel"])
+ keys = {"nbL", "lhankel", "version", "partial", "sparse", "build_from_sample", "ndEx"}
+ if not keys.issubset(set(data.keys())):
+ raise ValueError("The input data string (" + data_str +
+ ") should contain the following keys : \"" +
+ '\", \"'.join(keys) + "\"")
+ H = Hankel(version=data["version"], partial=data["partial"], sparse=data["sparse"],
+ lhankel = [Serializer.__restore_yaml(k) for k in data["lhankel"]])
+ if data["build_from_sample"]:
+ H.build_from_sample = True
+ H.nbL = data["nbL"]
+ H.nbEx = data["nbEx"]
+ return H
return data_str
@staticmethod
diff --git a/splearn/tests/datasets/simple_example.json b/splearn/tests/datasets/simple_example.json
new file mode 100644
index 0000000000000000000000000000000000000000..fdf1119cb581e77b9dab380d5c7a4c0bd4b7e48c
--- /dev/null
+++ b/splearn/tests/datasets/simple_example.json
@@ -0,0 +1 @@
+{"automaton": {"nbL": 2, "nbS": 2, "initial": {"numpy.ndarray": {"values": [0.5, 0.5], "dtype": "float64"}}, "final": {"numpy.ndarray": {"values": [0.5, 0.16666666666666666], "dtype": "float64"}}, "transitions": [{"numpy.ndarray": {"values": [[0.16666666666666666, 0.08333333333333333], [0.16666666666666666, 0.16666666666666666]], "dtype": "float64"}}, {"numpy.ndarray": {"values": [[0.0, 0.25], [0.16666666666666666, 0.3333333333333333]], "dtype": "float64"}}], "type": "classic"}}
\ No newline at end of file
diff --git a/splearn/tests/datasets/simple_example.yaml b/splearn/tests/datasets/simple_example.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6e2a7ec6f9138cc441f796bd596b7636b19a419a
--- /dev/null
+++ b/splearn/tests/datasets/simple_example.yaml
@@ -0,0 +1,23 @@
+automaton:
+ final:
+ numpy.ndarray:
+ dtype: float64
+ values: [0.5, 0.16666666666666666]
+ initial:
+ numpy.ndarray:
+ dtype: float64
+ values: [0.5, 0.5]
+ nbL: 2
+ nbS: 2
+ transitions:
+ - numpy.ndarray:
+ dtype: float64
+ values:
+ - [0.16666666666666666, 0.08333333333333333]
+ - [0.16666666666666666, 0.16666666666666666]
+ - numpy.ndarray:
+ dtype: float64
+ values:
+ - [0.0, 0.25]
+ - [0.16666666666666666, 0.3333333333333333]
+ type: classic
diff --git a/splearn/tests/test_serializer.py b/splearn/tests/test_serializer.py
new file mode 100644
index 0000000000000000000000000000000000000000..524a7c9536ff2b1411e417ebf7c4e2e2c3a2516f
--- /dev/null
+++ b/splearn/tests/test_serializer.py
@@ -0,0 +1,50 @@
+# -*- coding: utf-8 -*-
+import unittest
+import numpy as np
+import filecmp
+import os
+
+from splearn.automaton import Automaton
+from splearn.hankel import Hankel
+from splearn.tests.datasets.get_dataset_path import get_dataset_path
+
+class UnitaryTest(unittest.TestCase):
+
+
+ def setUp(self):
+ self.A = Automaton.SimpleExample()
+ self.title = "Simple Example"
+ self.input_file = 'simple_example'
+ self.formats = ['json', 'yaml']
+ self.words = [[], [0], [0, 0], [1], [1, 1], [0, 1, 0], [1, 0, 1]]
+ self.d_str = "Value for word \"{:s}\" = {:.2f}"
+
+ def testReadAutomaton(self):
+ for f in self.formats:
+ B = Automaton.read(get_dataset_path(self.input_file + '.' + f), format=f)
+ for w in self.words:
+ np.testing.assert_almost_equal(self.A.val(w), B.val(w))
+
+ def testWriteAutomata(self):
+ for f in self.formats:
+ Automaton.write(self.A, get_dataset_path(self.input_file + '_2.' + f), format=f)
+ self.assertTrue(filecmp.cmp(get_dataset_path(self.input_file + '_2.' + f),
+ get_dataset_path(self.input_file + '.' + f)))
+ for f in self.formats:
+ os.remove(get_dataset_path(self.input_file + '_2.' + f))
+
+ def testReadHankel(self):
+ for f in self.formats:
+ H = self.A.to_hankel([(), (0,), (1,)], [(), (0,), (1,)])
+ Hankel.write(H, get_dataset_path(self.input_file + "_hankel" + "." + f), format=f)
+ Hb = Hankel.read(get_dataset_path(self.input_file + "_hankel" + "." + f), format = f)
+ B = Hb.to_automaton(2)
+ for w in self.words:
+ np.testing.assert_almost_equal(self.A.val(w), B.val(w))
+ for f in self.formats:
+ os.remove(get_dataset_path(self.input_file + "_hankel" + "." + f))
+
+
+if __name__ == "__main__":
+ #import sys;sys.argv = ['', 'Test.testName']
+ unittest.main()
\ No newline at end of file