diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index fd56bbf8ae66b6d594bba8b57685c1ead4f7cbe2..cda6a4fe543650d59833a0424085d436a843c59a 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -9,7 +9,7 @@ unbutu:17.10:
- pip3 install -e .
- nosetests
- python3 setup.py build_sphinx
- - cp -r build/sphinx/html/* public/
+ - cp -r build/sphinx/html public
artifacts:
when: always
untracked: true
diff --git a/examples/3.pautomac_light.train.json b/examples/3.pautomac_light.train.json
index 56415b8ee3cc099afc1dc377605708d8a08860b4..9f45619b654f3e643a0478fb6e237b7b406fa8a3 100644
--- a/examples/3.pautomac_light.train.json
+++ b/examples/3.pautomac_light.train.json
@@ -1 +1 @@
-{"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
+{"automaton": {"nbL": 4, "nbS": 5, "initial": {"numpy.ndarray": {"values": [-0.0004934419970497512, 0.0030634697107912346, -0.044073932015580415, -0.1077770261654714, -0.0866391379316952], "dtype": "float64"}}, "final": {"numpy.ndarray": {"values": [0.07757136847945045, -0.024220294003132026, -0.4468125366321221, 0.627732084089759, -0.554674433356224], "dtype": "float64"}}, "transitions": [{"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"}}, {"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"}}, {"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"}}, {"numpy.ndarray": {"values": [[0.07276211427780357, -0.0157195576855797, 0.07428592814590385, -0.10369861539249735, 0.024753473688328077], [-0.05607105449779142, -0.08896207276035666, 0.27638225397521243, -0.2371125582838589, 0.07372294122306285], [-0.007391294007753122, -0.048741797963875705, -0.6291239733858526, 0.46816276521577677, 0.09251699239093385], [-0.007110224931878467, -0.05623317735898056, -0.36606658567620365, -0.013297798115225407, 0.6491033177492604], [0.002335515008556511, -0.021561151264484414, 0.09096243479437888, -0.38438823493062646, 0.6616477207948602]], "dtype": "float64"}}], "type": "classic"}}
\ No newline at end of file
diff --git a/examples/3.pautomac_light.train.yaml b/examples/3.pautomac_light.train.yaml
index c71fe8099efcf0c5e68836c5b24d6936c5a92cbe..09108d5a4dba3467ec25520429c52b27916876dc 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: [0.07757136847945678, -0.024220294003121035, -0.4468125366321232, 0.6277320840897611,
- -0.5546744333562244]
+ values: [0.07757136847945045, -0.024220294003132026, -0.4468125366321221, 0.627732084089759,
+ -0.554674433356224]
initial:
numpy.ndarray:
dtype: float64
- values: [-0.000493441997049692, 0.003063469710791502, -0.04407393201558057,
- -0.10777702616547158, -0.0866391379316951]
+ values: [-0.0004934419970497512, 0.0030634697107912346, -0.044073932015580415,
+ -0.1077770261654714, -0.0866391379316952]
nbL: 4
nbS: 5
transitions:
- numpy.ndarray:
dtype: float64
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]
+ - [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]
- numpy.ndarray:
dtype: float64
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]
+ - [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]
- numpy.ndarray:
dtype: float64
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]
+ - [-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]
- numpy.ndarray:
dtype: float64
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]
+ - [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]
type: classic
diff --git a/examples/json_save.ipynb b/examples/json_save.ipynb
index d42284d42a8fee69e4d145156c91fb7cb05c0d68..61db044db842d9e54fd1ed9cfb5ce9cfb4f5b629 100644
--- a/examples/json_save.ipynb
+++ b/examples/json_save.ipynb
@@ -101,7 +101,7 @@
{
"data": {
"text/plain": [
- "'{\"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": 7,
@@ -121,7 +121,7 @@
{
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},
"execution_count": 8,
@@ -155,68 +155,68 @@
" final:\n",
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" nbL: 4\n",
" nbS: 5\n",
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- " 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",
+ " - [0.07276211427780357, -0.0157195576855797, 0.07428592814590385, -0.10369861539249735,\n",
+ " 0.024753473688328077]\n",
+ " - [-0.05607105449779142, -0.08896207276035666, 0.27638225397521243, -0.2371125582838589,\n",
+ " 0.07372294122306285]\n",
+ " - [-0.007391294007753122, -0.048741797963875705, -0.6291239733858526, 0.46816276521577677,\n",
+ " 0.09251699239093385]\n",
+ " - [-0.007110224931878467, -0.05623317735898056, -0.36606658567620365, -0.013297798115225407,\n",
+ " 0.6491033177492604]\n",
+ " - [0.002335515008556511, -0.021561151264484414, 0.09096243479437888, -0.38438823493062646,\n",
+ " 0.6616477207948602]\n",
" type: classic\n",
"\n"
]
@@ -331,12 +331,356 @@
"Ayl.transitions"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from splearn import Hankel\n",
+ "Hankel.write(sp.hankel, train_file + \"_hankel.json\", \"json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Hb = Hankel.read(train_file + \"_hankel.json\", \"json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Hankel equality check\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Hb == sp.hankel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<1310x3308 sparse matrix of type '<class 'numpy.float64'>'\n",
+ "\twith 8251 stored elements in Dictionary Of Keys format>,\n",
+ " <1310x3308 sparse matrix of type '<class 'numpy.float64'>'\n",
+ "\twith 2199 stored elements in Dictionary Of Keys format>,\n",
+ " <1310x3308 sparse matrix of type '<class 'numpy.float64'>'\n",
+ "\twith 2122 stored elements in Dictionary Of Keys format>,\n",
+ " <1310x3308 sparse matrix of type '<class 'numpy.float64'>'\n",
+ "\twith 1091 stored elements in Dictionary Of Keys format>,\n",
+ " <1310x3308 sparse matrix of type '<class 'numpy.float64'>'\n",
+ "\twith 3489 stored elements in Dictionary Of Keys format>]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Hb.lhankel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<1310x3308 sparse matrix of type '<class 'numpy.float64'>'\n",
+ "\twith 8251 stored elements in Dictionary Of Keys format>,\n",
+ " <1310x3308 sparse matrix of type '<class 'numpy.float64'>'\n",
+ "\twith 2199 stored elements in Dictionary Of Keys format>,\n",
+ " <1310x3308 sparse matrix of type '<class 'numpy.float64'>'\n",
+ "\twith 2122 stored elements in Dictionary Of Keys format>,\n",
+ " <1310x3308 sparse matrix of type '<class 'numpy.float64'>'\n",
+ "\twith 1091 stored elements in Dictionary Of Keys format>,\n",
+ " <1310x3308 sparse matrix of type '<class 'numpy.float64'>'\n",
+ "\twith 3489 stored elements in Dictionary Of Keys format>]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sp.hankel.lhankel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import scipy.sparse as sps\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = sp.hankel.lhankel[0]\n",
+ "k_str = \"({0:d},{1:d})\"\n",
+ "dico = dict(zip([k_str.format(i, j) for (i,j) in data.keys()], data.values()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dok = sps.dok_matrix(data.shape, dtype=data.dtype)\n",
+ "for k, val in dico.items():\n",
+ " k = k.replace(\"(\",\"\").replace(\")\",\"\")\n",
+ " ind1, ind2 = k.split(\",\")\n",
+ " dok[(int(ind1), int(ind2))] = val"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " (0, 2950)\t6.0\n",
+ " (1, 1141)\t6.0\n",
+ " (2, 820)\t6.0\n",
+ " (9, 192)\t6.0\n",
+ " (35, 75)\t6.0\n",
+ " (123, 12)\t6.0\n",
+ " (358, 4)\t6.0\n",
+ " (832, 0)\t6.0\n",
+ " (0, 20)\t551.0\n",
+ " (1, 4)\t551.0\n",
+ " (5, 0)\t551.0\n",
+ " (0, 837)\t9.0\n",
+ " (1, 212)\t9.0\n",
+ " (4, 33)\t9.0\n",
+ " (14, 17)\t9.0\n",
+ " (56, 1)\t9.0\n",
+ " (183, 0)\t9.0\n",
+ " (0, 254)\t7.0\n",
+ " (1, 25)\t7.0\n",
+ " (2, 9)\t7.0\n",
+ " (7, 1)\t7.0\n",
+ " (26, 0)\t7.0\n",
+ " (0, 3160)\t5.0\n",
+ " (1, 1601)\t5.0\n",
+ " (5, 323)\t5.0\n",
+ " :\t:\n",
+ " (607, 109)\t1.0\n",
+ " (1270, 48)\t1.0\n",
+ " (34, 2382)\t1.0\n",
+ " (117, 1262)\t1.0\n",
+ " (336, 580)\t1.0\n",
+ " (761, 265)\t1.0\n",
+ " (464, 3272)\t1.0\n",
+ " (1015, 1821)\t1.0\n",
+ " (338, 2911)\t1.0\n",
+ " (770, 1090)\t1.0\n",
+ " (0, 2926)\t1.0\n",
+ " (1, 1113)\t1.0\n",
+ " (2, 767)\t1.0\n",
+ " (9, 131)\t1.0\n",
+ " (34, 70)\t1.0\n",
+ " (119, 7)\t1.0\n",
+ " (343, 3)\t1.0\n",
+ " (786, 0)\t1.0\n",
+ " (1073, 2555)\t1.0\n",
+ " (0, 825)\t1.0\n",
+ " (1, 197)\t1.0\n",
+ " (3, 80)\t1.0\n",
+ " (13, 17)\t1.0\n",
+ " (53, 1)\t1.0\n",
+ " (175, 0)\t1.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(dok)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from splearn import Hankel\n",
+ "Hankel.write(sp.hankel, train_file + \"_hankel.yaml\", \"yaml\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Hb = Hankel.read(train_file + \"_hankel.yaml\", \"yaml\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Hankel equality check\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Hb == sp.hankel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "yamlstr = \"- scipy.dok_matrix:\\n dtype: float64\\n shape:\\n tuple: [1, 1]\\n values: {'(0,0)': 1.0}\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "- scipy.dok_matrix:\n",
+ " dtype: float64\n",
+ " shape:\n",
+ " tuple: [1, 1]\n",
+ " values: {'(0,0)': 1.0}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(yamlstr)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<1x1 sparse matrix of type '<class 'numpy.float64'>'\n",
+ "\twith 1 stored elements in Dictionary Of Keys format>]"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Serializer.yaml_to_data(yamlstr)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'\"- scipy.dok_matrix:\\\\n dtype: float64\\\\n shape:\\\\n tuple: [1, 1]\\\\n values:\\\\\\n \\\\ {\\'(0,0)\\': 1.0}\"\\n'"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "issubclass(TypeError, ValueError)"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "\"The input data string (\" + data_str + \") should contain the following keys : \\\"\" + '\\\", \\\"'.join(keys) + \"\\\"\""
+ ]
}
],
"metadata": {
diff --git a/splearn/hankel.py b/splearn/hankel.py
index faff8619253acd72e824c4efcdfb18e9e3d5d06e..e9f0d844a0a09ed3a149c24ea090afe36785ac0f 100644
--- a/splearn/hankel.py
+++ b/splearn/hankel.py
@@ -111,6 +111,31 @@ class Hankel(object):
else:
raise ValueError("At least sample_instance or lhankel has to be not None.")
+ def __eq__(self, other):
+ print("Hankel equality check")
+ if self.version != other.version:
+ print("different versions")
+ return False
+ if self.partial != other.partial:
+ return False
+ if self.sparse != other.sparse:
+ return False
+ if self.build_from_sample != other.build_from_sample:
+ return False
+ if self.nbL != other.nbL:
+ return False
+ if self.nbEx != other.nbEx:
+ return False
+ if len(self.lhankel) != len(other.lhankel):
+ return False
+ for lh1, lh2 in zip(self.lhankel, other.lhankel):
+ if (lh1 != lh2).nnz > 0:
+ return False
+ return True
+
+ def __ne__(self, other):
+ return not self.__eq__(other)
+
@property
def nbL(self):
"""Number of letters"""
diff --git a/splearn/serializer.py b/splearn/serializer.py
index f027c9c58fa329bad58a6ed7cee55500422efccd..f7da0e5769ce24e24c1480099b5a3ea97fca149c 100644
--- a/splearn/serializer.py
+++ b/splearn/serializer.py
@@ -35,12 +35,11 @@
# ######### COPYRIGHT #########
"""This module contains the Serializer class
"""
-
import numpy as np
from splearn.automaton import Automaton
from splearn.hankel import Hankel
-from numpy.f2py.common_rules import findcommonblocks
+import scipy.sparse as sps
class Serializer(object):
""" Serializer is an helping object for data serialization
@@ -49,9 +48,16 @@ class Serializer(object):
@staticmethod
def __serialize(data):
if data is None or isinstance(data, (bool, int, float, str)):
+ if type(data).__module__ == "numpy":
+ return np.asscalar(data)
return data
if isinstance(data, list):
return [Serializer.__serialize(val) for val in data]
+ if isinstance(data, sps.dok_matrix):
+ k_str = "({0:d},{1:d})"
+ return {"scipy.dok_matrix": {"shape" : Serializer.__serialize(data.shape), "dtype": str(data.dtype),
+ "values" : Serializer.__serialize(dict(zip([k_str.format(i,j) for (i,j) in data.keys()],
+ data.values())))}}
if isinstance(data, dict):
if all(isinstance(k, str) for k in data):
return {k: Serializer.__serialize(v) for k, v in data.items()}
@@ -71,7 +77,7 @@ class Serializer(object):
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}
+ "build_from_sample" : data.build_from_sample, "nbEx" : data.nbEx}
return {"hankel" : Serializer.__serialize(data_dict)}
raise TypeError("Type %s is not serializabled" % type(data))
@@ -83,11 +89,26 @@ class Serializer(object):
return tuple(data_str["tuple"])
if "set" in data_str:
return set(data_str["set"])
+ if "scipy.dok_matrix" in data_str:
+ data = data_str["scipy.dok_matrix"]
+ keys = {"shape", "dtype", "values"}
+ if not keys.issubset(set(data.keys())):
+ raise ValueError("The input data string (" + str(data_str) +
+ ") should contain the following keys : \"" +
+ '\", \"'.join(keys) + "\"")
+ values = Serializer.__restore_json(data["values"])
+ shape = Serializer.__restore_json(data["shape"])
+ dok = sps.dok_matrix(shape, dtype=data["dtype"])
+ for k, val in values.items():
+ k = k.replace("(","").replace(")","")
+ ind1, ind2 = k.split(",")
+ dok[(int(ind1), int(ind2))] = val
+ return dok
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 +
+ raise ValueError("The input data string (" + str(data_str) +
") should contain the following keys : \"" +
'\", \"'.join(keys) + "\"")
return np.array(data["values"], dtype=data["dtype"])
@@ -95,16 +116,16 @@ class Serializer(object):
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 +
+ raise ValueError("The input data string (" + str(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"])
if "hankel" in data_str:
data = Serializer.__restore_json(data_str["hankel"])
- keys = {"nbL", "lhankel", "version", "partial", "sparse", "build_from_sample", "ndEx"}
+ keys = {"nbL", "lhankel", "version", "partial", "sparse", "build_from_sample", "nbEx"}
if not keys.issubset(set(data.keys())):
- raise ValueError("The input data string (" + data_str +
+ raise ValueError("The input data string (" + str(data_str) +
") should contain the following keys : \"" +
'\", \"'.join(keys) + "\"")
H = Hankel(version=data["version"], partial=data["partial"], sparse=data["sparse"],
@@ -129,11 +150,30 @@ class Serializer(object):
return tuple(data_str["tuple"])
if "set" in data_str:
return set(data_str["set"])
+ if "scipy.dok_matrix" in data_str:
+ data = data_str["scipy.dok_matrix"]
+ keys = {"shape", "dtype", "values"}
+ if not keys.issubset(set(data.keys())):
+ raise ValueError("The input data string (" + str(data_str) +
+ ") should contain the following keys : \"" +
+ '\", \"'.join(keys) + "\"")
+ values = Serializer.__restore_json(data["values"])
+ shape = Serializer.__restore_json(data["shape"])
+ dok = sps.dok_matrix(shape, dtype=data["dtype"])
+ for k, val in values.items():
+ k = k.replace("(","").replace(")","")
+ ind1, ind2 = k.split(",")
+ dok[(int(ind1), int(ind2))] = val
+ return dok
if "numpy.ndarray" in data_str:
data = data_str["numpy.ndarray"]
keys = {"values", "dtype"}
+ if data is None:
+ raise ValueError("The input data string (" + str(data_str) +
+ ") should contain the following keys : \"" +
+ '\", \"'.join(keys) + "\"")
if not keys.issubset(set(data.keys())):
- raise ValueError("The input data string (" + data_str +
+ raise ValueError("The input data string (" + str(data_str) +
") should contain the following keys : \"" +
'\", \"'.join(keys) + "\"")
return np.array(data["values"], dtype=data["dtype"])
@@ -141,7 +181,7 @@ class Serializer(object):
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 +
+ raise ValueError("The input data string (" + str(data_str) +
") should contain the following keys : \"" +
'\", \"'.join(keys) + "\"")
return Automaton(nbL=data["nbL"], nbS=data["nbS"], initial=Serializer.__restore_yaml(data["initial"]),
@@ -150,9 +190,9 @@ class Serializer(object):
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"}
+ keys = {"nbL", "lhankel", "version", "partial", "sparse", "build_from_sample", "nbEx"}
if not keys.issubset(set(data.keys())):
- raise ValueError("The input data string (" + data_str +
+ raise ValueError("The input data string (" + str(data_str) +
") should contain the following keys : \"" +
'\", \"'.join(keys) + "\"")
H = Hankel(version=data["version"], partial=data["partial"], sparse=data["sparse"],
diff --git a/splearn/tests/test_serializer.py b/splearn/tests/test_serializer.py
index 3a510140b2627983e9881ad138d74957a90394a1..aa80ad1bd0c6b5b8de4d4a7ac589298265d49764 100644
--- a/splearn/tests/test_serializer.py
+++ b/splearn/tests/test_serializer.py
@@ -36,6 +36,8 @@
import unittest
import numpy as np
import os
+from collections import deque
+import yaml
from splearn.automaton import Automaton
from splearn.hankel import Hankel
@@ -81,21 +83,19 @@ class UnitaryTest(unittest.TestCase):
for f in self.formats:
os.remove(get_dataset_path(self.input_file + "_hankel" + "." + f))
-# def testReadWriteRealHankel(self):
-# adr = get_dataset_path("3.pautomac.train")
-# data = load_data_sample(adr=adr)
-# X = data.data
-# sp = Spectral()
-# sp = sp.fit(X)
-# H = Hankel( sample_instance=X.sample,
-# lrows=6, lcolumns=6, version="classic",
-# partial=True, sparse=True, mode_quiet=True)
-# for f in self.formats:
-# Hankel.write(H, get_dataset_path("3.pautomac.train" + "_hankel" + "." + f), format=f)
-# Hb = Hankel.read(get_dataset_path("3.pautomac.train" + "_hankel" + "." + f), format = f)
-# self.assertEqual(H, Hb)
-# for f in self.formats:
-# os.remove(get_dataset_path("3.pautomac.train" + "_hankel" + "." + f))
+ def testReadWriteRealHankel(self):
+ adr = get_dataset_path("3.pautomac.train")
+ data = load_data_sample(adr=adr)
+ X = data.data
+ sp = Spectral()
+ sp = sp.fit(X)
+ H = sp.hankel
+ for f in self.formats:
+ Hankel.write(H, get_dataset_path("3.pautomac.train" + "_hankel" + "." + f), format=f)
+ Hb = Hankel.read(get_dataset_path("3.pautomac.train" + "_hankel" + "." + f), format = f)
+ self.assertTrue(H == Hb)
+ for f in self.formats:
+ os.remove(get_dataset_path("3.pautomac.train" + "_hankel" + "." + f))
def testOthersSerializationTypes(self):
data = [{'a' : 10, 40 : 'gu'}, {'toto', 5, 2.5, 'b'}, ('gh', 25, 'ko', 1.0)]
@@ -113,6 +113,27 @@ class UnitaryTest(unittest.TestCase):
data_yaml = Serializer.yaml_to_data(data_yaml_str)
self.assertEqual(data, data_json)
self.assertEqual(data, data_yaml)
+
+ def testBadTypeSerialieationException(self):
+ with self.assertRaises(TypeError):
+ Serializer.data_to_json(deque('ghi'))
+
+ def testBadDataException(self):
+ yamlstr = "- scipy.dok_matrix:\n shape:\n tuple: [1, 1]\n values: {'(0,0)': 1.0}"
+ with self.assertRaises(ValueError):
+ Serializer.yaml_to_data(yamlstr)
+ jsonstr = "{\"scipy.dok_matrix\":{}}"
+ with self.assertRaises(ValueError):
+ Serializer.json_to_data(jsonstr)
+ yamlstr = "- numpy.ndarray:"
+ with self.assertRaises(ValueError):
+ Serializer.yaml_to_data(yamlstr)
+ yamlstr = "- numpy.ndarray:\n dtype: float64"
+ with self.assertRaises(ValueError):
+ Serializer.yaml_to_data(yamlstr)
+ jsonstr = "{\"numpy.ndarray\":{}}"
+ with self.assertRaises(ValueError):
+ Serializer.json_to_data(jsonstr)
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testName']