diff --git a/examples/3.pautomac_light.train.gv b/examples/3.pautomac_light.train.gv
deleted file mode 100644
index 630afa0c022bca2912dd7c273fa6247b5aacb683..0000000000000000000000000000000000000000
--- a/examples/3.pautomac_light.train.gv
+++ /dev/null
@@ -1,41 +0,0 @@
-//3.pautomac_light.train.dot
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diff --git a/examples/3.pautomac_light.train.gv.pdf b/examples/3.pautomac_light.train.gv.pdf
deleted file mode 100644
index 13325f666807d2f6f0e6e5f37700589d688fa25b..0000000000000000000000000000000000000000
Binary files a/examples/3.pautomac_light.train.gv.pdf and /dev/null differ
diff --git a/examples/3.pautomac_light.train.json b/examples/3.pautomac_light.train.json
deleted file mode 100644
index 9f45619b654f3e643a0478fb6e237b7b406fa8a3..0000000000000000000000000000000000000000
--- a/examples/3.pautomac_light.train.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 09108d5a4dba3467ec25520429c52b27916876dc..0000000000000000000000000000000000000000
--- a/examples/3.pautomac_light.train.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-automaton:
- final:
- numpy.ndarray:
- dtype: float64
- values: [0.07757136847945045, -0.024220294003132026, -0.4468125366321221, 0.627732084089759,
- -0.554674433356224]
- initial:
- numpy.ndarray:
- dtype: float64
- values: [-0.0004934419970497512, 0.0030634697107912346, -0.044073932015580415,
- -0.1077770261654714, -0.0866391379316952]
- nbL: 4
- nbS: 5
- transitions:
- - numpy.ndarray:
- dtype: float64
- 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]
- - numpy.ndarray:
- dtype: float64
- 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]
- - numpy.ndarray:
- dtype: float64
- 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]
- - numpy.ndarray:
- dtype: float64
- 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]
- type: classic
diff --git a/examples/PythonOptimizations.ipynb b/examples/PythonOptimizations.ipynb
deleted file mode 100644
index 386c4d339a122e7cad01df2a0be4a8152f21b165..0000000000000000000000000000000000000000
--- a/examples/PythonOptimizations.ipynb
+++ /dev/null
@@ -1,394 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from timeit import default_timer as timer\n",
- "import random\n",
- "import numpy as np"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "*** With setdefault ***\n",
- "Mean time = 2.9573e-07 +/- 5.1021e-07 sigma\n",
- "{33: 969, 23: 1030, 10: 1002, 54: 990, 37: 1055, 28: 983, 34: 964, 25: 1023, 55: 1016, 46: 958, 17: 979, 0: 1003, 98: 1025, 19: 977, 66: 1002, 73: 979, 42: 1038, 52: 986, 80: 1007, 45: 1044, 62: 942, 40: 1001, 77: 996, 26: 995, 11: 971, 97: 985, 59: 1024, 63: 939, 43: 1036, 88: 977, 47: 967, 44: 964, 93: 967, 51: 962, 69: 1003, 12: 988, 81: 1008, 82: 962, 61: 1003, 30: 985, 3: 1017, 79: 975, 29: 979, 35: 989, 31: 1019, 72: 1017, 78: 977, 100: 977, 7: 947, 91: 973, 90: 966, 38: 960, 20: 927, 8: 983, 75: 999, 92: 1020, 99: 1030, 64: 976, 57: 1030, 86: 1041, 16: 941, 39: 1017, 94: 996, 18: 940, 13: 1024, 27: 990, 53: 1042, 68: 952, 2: 1031, 85: 984, 21: 1009, 36: 1009, 71: 990, 84: 1022, 49: 1010, 60: 990, 70: 967, 9: 949, 67: 989, 87: 980, 74: 970, 32: 983, 89: 999, 96: 998, 50: 991, 56: 1010, 76: 1001, 95: 964, 24: 1004, 14: 950, 83: 1053, 5: 988, 22: 1043, 65: 980, 6: 967, 15: 966, 41: 922, 1: 960, 48: 958, 58: 955, 4: 999}\n",
- "*** With if syntax ***\n",
- "Mean time = 2.9544e-07 +/- 5.2105e-07 sigma\n",
- "{33: 969, 23: 1030, 10: 1002, 54: 990, 37: 1055, 28: 983, 34: 964, 25: 1023, 55: 1016, 46: 958, 17: 979, 0: 1003, 98: 1025, 19: 977, 66: 1002, 73: 979, 42: 1038, 52: 986, 80: 1007, 45: 1044, 62: 942, 40: 1001, 77: 996, 26: 995, 11: 971, 97: 985, 59: 1024, 63: 939, 43: 1036, 88: 977, 47: 967, 44: 964, 93: 967, 51: 962, 69: 1003, 12: 988, 81: 1008, 82: 962, 61: 1003, 30: 985, 3: 1017, 79: 975, 29: 979, 35: 989, 31: 1019, 72: 1017, 78: 977, 100: 977, 7: 947, 91: 973, 90: 966, 38: 960, 20: 927, 8: 983, 75: 999, 92: 1020, 99: 1030, 64: 976, 57: 1030, 86: 1041, 16: 941, 39: 1017, 94: 996, 18: 940, 13: 1024, 27: 990, 53: 1042, 68: 952, 2: 1031, 85: 984, 21: 1009, 36: 1009, 71: 990, 84: 1022, 49: 1010, 60: 990, 70: 967, 9: 949, 67: 989, 87: 980, 74: 970, 32: 983, 89: 999, 96: 998, 50: 991, 56: 1010, 76: 1001, 95: 964, 24: 1004, 14: 950, 83: 1053, 5: 988, 22: 1043, 65: 980, 6: 967, 15: 966, 41: 922, 1: 960, 48: 958, 58: 955, 4: 999}\n"
- ]
- }
- ],
- "source": [
- "N = 100000\n",
- "d1 = {}\n",
- "d2 = {}\n",
- "duration1 = np.zeros((N,), dtype=np.float32)\n",
- "duration2 = np.zeros((N,), dtype=np.float32)\n",
- "str = \"Mean time = {0:.4e} +/- {1:.4e} sigma\"\n",
- "for i in range(N):\n",
- " k = random.randint(0,100)\n",
- " start = timer()\n",
- " d1[k] = d1.setdefault(k, 0) + 1\n",
- " duration1[i] = timer() - start\n",
- " start = timer()\n",
- " d2[k] = d2[k] + 1 if k in d2 else 1\n",
- " duration2[i] = timer() - start\n",
- "\n",
- "print(\"*** With setdefault ***\")\n",
- "print(str.format(np.mean(duration1), np.std(duration1)))\n",
- "print(d1)\n",
- "print(\"*** With if syntax ***\")\n",
- "print(str.format(np.mean(duration2), np.std(duration2)))\n",
- "print(d2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "__global__ void multiply_them(float *dest, float *a, float *b)\n",
- "{\n",
- " const int i = threadIdx.x;\n",
- " dest[i] = a[i] * b[i];\n",
- "}\n",
- "\n",
- "[-2.90137202e-01 2.81293893e+00 -1.65957522e+00 -8.62088725e-02\n",
- " 1.45804667e+00 7.99023032e-01 8.37303638e-01 2.03320041e-01\n",
- " -1.78530657e+00 -9.88828689e-02 2.83610914e-02 -3.73530895e-01\n",
- " 1.13282132e+00 3.41161788e-01 7.03967333e-01 2.51203799e+00\n",
- " -1.10995814e-01 -2.47637033e-01 4.86121893e-01 -5.22908807e-01\n",
- " 2.42040649e-01 3.50897670e-01 -2.33534321e-01 -5.71392417e-01\n",
- " 2.94612437e-01 8.51076543e-01 -1.22819483e-01 -1.12457033e-02\n",
- " 7.62222052e-01 -5.62664986e-01 -2.81204749e-02 2.15141201e+00\n",
- " -2.52846658e-01 -4.59794961e-02 -2.25618288e-01 -2.16486081e-02\n",
- " 1.80242336e+00 1.98668197e-01 1.38681874e-01 -8.27464104e-01\n",
- " 2.84924650e+00 4.41990757e+00 7.45217443e-01 2.79382646e-01\n",
- " -3.47226970e-02 -3.42106796e-03 -2.03399825e+00 1.97560683e-01\n",
- " 1.39831769e+00 -8.06550503e-01 -1.33297205e+00 1.37708103e-02\n",
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- " -2.13610873e-01 1.05494165e+00 1.07709356e-02 -4.04108614e-02\n",
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- " 1.53846651e-01 1.16461468e+00 -4.88103539e-01 -2.46520080e-02\n",
- " -7.18142092e-02 -1.67339086e-03 -2.42161244e-01 2.67475009e-01\n",
- " -1.32814407e+00 -5.36984839e-02 1.15024351e-01 -1.35336888e+00\n",
- " -4.01394248e-01 2.28310853e-01 2.85696723e-02 -9.75638449e-01\n",
- " 1.79381263e+00 1.34870076e+00 -9.75736082e-01 -2.56683826e-01\n",
- " -9.24404740e-01 1.18486941e+00 -1.60593867e-01 5.32781780e-01\n",
- " -1.61111280e-01 -5.70979357e-01 2.48478189e-01 -5.70544958e-01\n",
- " -3.36045325e-02 -1.70751661e-01 -7.83387959e-01 -3.28783333e-01\n",
- " 6.68492764e-02 -4.55635548e-01 6.78718448e-01 1.61884940e+00\n",
- " 1.40411711e+00 1.68428612e+00 -1.77249038e+00 -9.48907316e-01\n",
- " 1.18341064e+00 -5.30112609e-02 -1.22148085e+00 5.62596917e-01\n",
- " 1.31391573e+00 1.84780562e+00 5.45549512e-01 1.03463089e+00\n",
- " -7.32520103e-01 3.38242576e-02 -6.29517257e-01 6.84408307e-01\n",
- " -2.80194193e-01 4.18214053e-01 -2.41577148e+00 -2.93253827e+00\n",
- " 9.50718462e-01 2.36972594e+00 4.81406689e-01 1.71889886e-02\n",
- " 6.07256055e-01 -2.35717386e-01 -2.49700022e+00 6.08527958e-01\n",
- " -7.02820182e-01 -1.22066826e-01 6.92340672e-01 9.00277853e-01\n",
- " -1.30328491e-01 -4.41209674e-02 -9.21233058e-01 1.37919143e-01\n",
- " 3.67532313e-01 3.77332382e-02 2.87247807e-01 2.75621843e+00\n",
- " 7.48364270e-01 -3.02746415e-01 1.44414037e-01 5.80033548e-02\n",
- " 1.95701897e-01 -1.33819908e-01 1.95749756e-02 9.36418235e-01\n",
- " 1.13439392e-02 -3.80111992e-01 -9.90576968e-02 -5.11335442e-03\n",
- " -7.68416934e-03 -2.29034567e+00 -2.59990782e-01 -1.78645715e-01\n",
- " 1.65659058e+00 -2.18677855e+00 1.03389049e+00 1.44531997e-02\n",
- " -3.51684600e-01 4.83583333e-03 1.80972859e-01 2.64298506e-02\n",
- " -2.43892953e-01 -2.05088049e-01 -3.25786471e-01 6.42316818e-01\n",
- " 1.44556820e-01 9.11835134e-02 1.94504070e+00 -5.24064781e-05\n",
- " -2.08403915e-01 -1.54737011e-01 -1.55257428e+00 7.24277273e-02\n",
- " -8.63651395e-01 1.59108377e+00 5.03697515e-01 -3.29254329e-01\n",
- " -8.90930295e-02 1.14660770e-01 -9.05855417e-01 8.15455243e-03\n",
- " 1.48153394e-01 -2.29722723e-01 -8.32916439e-01 -6.75686240e-01\n",
- " 6.29307210e-01 -5.49214065e-01 -8.63798857e-02 -2.25813970e-01\n",
- " 1.34750354e+00 -1.27279890e+00 1.71834212e-02 -9.28580642e-01\n",
- " -4.82821971e-01 -3.34665596e-01 -5.94868183e-01 -1.29932821e-01\n",
- " -1.92791373e-02 -1.49232015e-01 1.34774446e+00 1.19024850e-01\n",
- " -1.20160818e-01 -1.26262501e-01 1.97318313e-03 1.36541653e+00\n",
- " 1.02985278e-01 -2.10293263e-01 2.37391043e+00 2.78081983e-01\n",
- " 1.06245232e+00 -2.89069340e-02 -2.64283836e-01 -3.76625746e-01\n",
- " 2.17229322e-01 4.31653678e-01 3.62828434e-01 5.05672574e-01\n",
- " 7.73309097e-02 2.52127171e+00 -5.13727009e-01 -5.77548921e-01\n",
- " -1.68320909e-01 4.10304099e-01 2.59571910e-01 2.76594549e-01\n",
- " 1.45417917e+00 4.71246615e-02 -9.45373952e-01 -2.64028404e-02\n",
- " -1.45785689e+00 -2.15390992e+00 2.60196701e-02 -1.00632882e+00\n",
- " -2.80741006e-01 -1.05663754e-01 -1.99056208e-01 1.07909453e+00]\n"
- ]
- }
- ],
- "source": [
- "import pycuda.autoinit\n",
- "import pycuda.driver as drv\n",
- "import numpy\n",
- "from pycuda.compiler import SourceModule\n",
- "\n",
- "module = \"\"\"\n",
- "__global__ void multiply_them(float *dest, float *a, float *b)\n",
- "{\n",
- " const int i = threadIdx.x;\n",
- " dest[i] = a[i] * b[i];\n",
- "}\n",
- "\"\"\"\n",
- "print(module)\n",
- "\n",
- "mod = SourceModule(module, nvcc=\"nvcc\", options=[\"-ccbin=/usr/bin/clang-3.8\"])\n",
- "\n",
- "multiply_them = mod.get_function(\"multiply_them\")\n",
- "\n",
- "a = numpy.random.randn(400).astype(numpy.float32)\n",
- "b = numpy.random.randn(400).astype(numpy.float32)\n",
- "\n",
- "dest = numpy.zeros_like(a)\n",
- "multiply_them(\n",
- " drv.Out(dest), drv.In(a), drv.In(b),\n",
- " block=(400,1,1), grid=(1,1))\n",
- "\n",
- "print (dest)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "ename": "CompileError",
- "evalue": "nvcc preprocessing of /tmp/tmpn_14m8_h.cu failed\n[command: nvcc --preprocess -arch sm_50 -I/home/arrivault/.virtualenvs/splearn-pycuda/lib/python3.6/site-packages/pycuda/cuda /tmp/tmpn_14m8_h.cu --compiler-options -P]\n[stderr:\nb\"ERROR: No supported gcc/g++ host compiler found, but clang-3.8 is available.\\n Use 'nvcc -ccbin clang-3.8' to use that instead.\\n\"]",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mCompileError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-4-1815e70115ee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpycuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurandom\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrand\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcurand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0ma_gpu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m50\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 8\u001b[0m \u001b[0mb_gpu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m50\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 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/splearn-pycuda/lib/python3.6/site-packages/pycuda/curandom.py\u001b[0m in \u001b[0;36mrand\u001b[0;34m(shape, dtype, stream)\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[0mdest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mPOW_2_M32\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 209\u001b[0m \"\"\",\n\u001b[0;32m--> 210\u001b[0;31m \"md5_rng_float\")\n\u001b[0m\u001b[1;32m 211\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 212\u001b[0m func = get_elwise_kernel(\n",
- "\u001b[0;32m~/.virtualenvs/splearn-pycuda/lib/python3.6/site-packages/pycuda/elementwise.py\u001b[0m in \u001b[0;36mget_elwise_kernel\u001b[0;34m(arguments, operation, name, keep, options, **kwargs)\u001b[0m\n\u001b[1;32m 159\u001b[0m \"\"\"\n\u001b[1;32m 160\u001b[0m func, arguments = get_elwise_kernel_and_types(\n\u001b[0;32m--> 161\u001b[0;31m arguments, operation, name, keep, options, **kwargs)\n\u001b[0m\u001b[1;32m 162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/splearn-pycuda/lib/python3.6/site-packages/pycuda/elementwise.py\u001b[0m in \u001b[0;36mget_elwise_kernel_and_types\u001b[0;34m(arguments, operation, name, keep, options, use_range, **kwargs)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 146\u001b[0m mod = module_builder(arguments, operation, name,\n\u001b[0;32m--> 147\u001b[0;31m keep, options, **kwargs)\n\u001b[0m\u001b[1;32m 148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/splearn-pycuda/lib/python3.6/site-packages/pycuda/elementwise.py\u001b[0m in \u001b[0;36mget_elwise_module\u001b[0;34m(arguments, operation, name, keep, options, preamble, loop_prep, after_loop)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;34m\"after_loop\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mafter_loop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m },\n\u001b[0;32m---> 75\u001b[0;31m options=options, keep=keep)\n\u001b[0m\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/splearn-pycuda/lib/python3.6/site-packages/pycuda/compiler.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, source, nvcc, options, keep, no_extern_c, arch, code, cache_dir, include_dirs)\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 290\u001b[0m cubin = compile(source, nvcc, options, keep, no_extern_c,\n\u001b[0;32m--> 291\u001b[0;31m arch, code, cache_dir, include_dirs)\n\u001b[0m\u001b[1;32m 292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpycuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdriver\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmodule_from_buffer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/splearn-pycuda/lib/python3.6/site-packages/pycuda/compiler.py\u001b[0m in \u001b[0;36mcompile\u001b[0;34m(source, nvcc, options, keep, no_extern_c, arch, code, cache_dir, include_dirs, target)\u001b[0m\n\u001b[1;32m 253\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-I\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcompile_plain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnvcc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mCudaModule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/splearn-pycuda/lib/python3.6/site-packages/pycuda/compiler.py\u001b[0m in \u001b[0;36mcompile_plain\u001b[0;34m(source, options, keep, nvcc, cache_dir, target)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'#include'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m \u001b[0mchecksum\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreprocess_source\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnvcc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\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 79\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0mchecksum\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/splearn-pycuda/lib/python3.6/site-packages/pycuda/compiler.py\u001b[0m in \u001b[0;36mpreprocess_source\u001b[0;34m(source, options, nvcc)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpycuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdriver\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCompileError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m raise CompileError(\"nvcc preprocessing of %s failed\" % source_path,\n\u001b[0;32m---> 55\u001b[0;31m cmdline, stderr=stderr)\n\u001b[0m\u001b[1;32m 56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;31m# sanity check\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mCompileError\u001b[0m: nvcc preprocessing of /tmp/tmpn_14m8_h.cu failed\n[command: nvcc --preprocess -arch sm_50 -I/home/arrivault/.virtualenvs/splearn-pycuda/lib/python3.6/site-packages/pycuda/cuda /tmp/tmpn_14m8_h.cu --compiler-options -P]\n[stderr:\nb\"ERROR: No supported gcc/g++ host compiler found, but clang-3.8 is available.\\n Use 'nvcc -ccbin clang-3.8' to use that instead.\\n\"]"
- ]
- }
- ],
- "source": [
- "import pycuda.gpuarray as gpuarray\n",
- "import pycuda.driver as cuda\n",
- "import pycuda.autoinit\n",
- "import numpy\n",
- "from pycuda.curandom import rand as curand\n",
- "\n",
- "a_gpu = curand((50,))\n",
- "b_gpu = curand((50,))\n",
- "\n",
- "from pycuda.elementwise import ElementwiseKernel\n",
- "lin_comb = ElementwiseKernel(\n",
- " \"float a, float *x, float b, float *y, float *z\",\n",
- " \"z[i] = a*x[i] + b*y[i]\",\n",
- " \"linear_combination\")\n",
- "\n",
- "c_gpu = gpuarray.empty_like(a_gpu)\n",
- "lin_comb(5, a_gpu, 6, b_gpu, c_gpu)\n",
- "\n",
- "import numpy.linalg as la\n",
- "assert la.norm((c_gpu - (5*a_gpu+6*b_gpu)).get()) < 1e-5"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from scipy.linalg import svd\n",
- "import pycuda.autoinit\n",
- "import pycuda.gpuarray as gpuarray\n",
- "d = 50\n",
- "A = np.asarray(np.random.randint(1, 10,(d, d)), dtype=np.float32)\n",
- "a_gpu = gpuarray.to_gpu(A)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "2.19 ms ± 1.49 ms per loop (mean ± std. dev. of 7 runs, 3 loops each)\n"
- ]
- }
- ],
- "source": [
- "%timeit -n3 [u, s, v] = svd(A)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "3.76 ms ± 1.95 ms per loop (mean ± std. dev. of 7 runs, 3 loops each)\n"
- ]
- }
- ],
- "source": [
- "%timeit -n3 [u, s, v] = np.linalg.svd(A)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "ename": "CUSOLVER_STATUS_INTERNAL_ERROR",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mCUSOLVER_STATUS_INTERNAL_ERROR\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-9-3d7440e3c764>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"-n3 u_gpu, s_gpu, vh_gpu = linalg.svd(a_gpu, 'S', 'S', 'cusolver')\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m~/.virtualenvs/sksplearn/lib/python3.6/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2093\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2094\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2095\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2096\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m<decorator-gen-61>\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/sksplearn/lib/python3.6/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/sksplearn/lib/python3.6/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 1100\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1102\u001b[0;31m \u001b[0mall_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1103\u001b[0m \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1104\u001b[0m \u001b[0mworst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/lib/python3.6/timeit.py\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0mr\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 205\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 207\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/sksplearn/lib/python3.6/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m<magic-timeit>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/sksplearn/build/scikit-cuda/skcuda/linalg.py\u001b[0m in \u001b[0;36msvd\u001b[0;34m(a_gpu, jobu, jobvt, lib)\u001b[0m\n\u001b[1;32m 467\u001b[0m \u001b[0mldu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvh_gpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgpudata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mldvt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 468\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgpudata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrwork\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 469\u001b[0;31m int(devInfo.gpudata))\n\u001b[0m\u001b[1;32m 470\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 471\u001b[0m \u001b[0;31m# Free working space:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/sksplearn/build/scikit-cuda/skcuda/cusolver.py\u001b[0m in \u001b[0;36mcusolverDnSgesvd\u001b[0;34m(handle, jobu, jobvt, m, n, a, lda, s, U, ldu, vt, ldvt, work, lwork, rwork, devInfo)\u001b[0m\n\u001b[1;32m 998\u001b[0m \u001b[0mldu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mldvt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwork\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 999\u001b[0m lwork, int(rwork), int(devInfo))\n\u001b[0;32m-> 1000\u001b[0;31m \u001b[0mcusolverCheckStatus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1001\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1002\u001b[0m \u001b[0m_libcusolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcusolverDnDgesvd_bufferSize\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/sksplearn/build/scikit-cuda/skcuda/cusolver.py\u001b[0m in \u001b[0;36mcusolverCheckStatus\u001b[0;34m(status)\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mCUSOLVER_EXCEPTIONS\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 163\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCUSOLVER_ERROR\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mCUSOLVER_STATUS_INTERNAL_ERROR\u001b[0m: "
- ]
- }
- ],
- "source": [
- "%timeit -n3 u_gpu, s_gpu, vh_gpu = linalg.svd(a_gpu, 'S', 'S', 'cusolver')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "ename": "CUSOLVERError",
- "evalue": "CUSOLVER_STATUS_INTERNAL_ERROR",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mCUSOLVERError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-8-c960eb315819>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-n3 u_cp, s_cp, v_cp = cp.linalg.svd(a_cp)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m~/.virtualenvs/sksplearn/lib/python3.6/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2093\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2094\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2095\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2096\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m<decorator-gen-61>\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/sksplearn/lib/python3.6/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/sksplearn/lib/python3.6/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 1100\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1102\u001b[0;31m \u001b[0mall_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1103\u001b[0m \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1104\u001b[0m \u001b[0mworst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/lib/python3.6/timeit.py\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0mr\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 205\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 207\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/sksplearn/lib/python3.6/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m<magic-timeit>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
- "\u001b[0;32m~/.virtualenvs/sksplearn/lib/python3.6/site-packages/cupy/linalg/decomposition.py\u001b[0m in \u001b[0;36msvd\u001b[0;34m(a, full_matrices, compute_uv)\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mptr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mptr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu_ptr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvt_ptr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m workspace.data.ptr, buffersize, 0, dev_info.data.ptr)\n\u001b[0m\u001b[1;32m 257\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# dtype == 'd'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 258\u001b[0m \u001b[0mbuffersize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcusolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdgesvd_bufferSize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32mcupy/cuda/cusolver.pyx\u001b[0m in \u001b[0;36mcupy.cuda.cusolver.sgesvd\u001b[0;34m()\u001b[0m\n",
- "\u001b[0;32mcupy/cuda/cusolver.pyx\u001b[0m in \u001b[0;36mcupy.cuda.cusolver.sgesvd\u001b[0;34m()\u001b[0m\n",
- "\u001b[0;32mcupy/cuda/cusolver.pyx\u001b[0m in \u001b[0;36mcupy.cuda.cusolver.check_status\u001b[0;34m()\u001b[0m\n",
- "\u001b[0;31mCUSOLVERError\u001b[0m: CUSOLVER_STATUS_INTERNAL_ERROR"
- ]
- }
- ],
- "source": [
- "%timeit -n3 u_cp, s_cp, v_cp = cp.linalg.svd(a_cp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/performances_calculation.py b/examples/performances_calculation.py
deleted file mode 100644
index 771fe0ba2c4f3c544921c60543d9b304c964b29c..0000000000000000000000000000000000000000
--- a/examples/performances_calculation.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# -*- coding: utf-8 -*-
-
-'''
-Created on 20 févr. 2018
-
-@author: arrivault
-'''
-from timeit import default_timer as timer
-
-from splearn import Spectral
-from splearn.tests.datasets.get_dataset_path import get_dataset_path
-from splearn.datasets.base import load_data_sample
-
-def launch_fit(X, version='classic', partial=True, sparse=True, smooth_method='none'):
- param = "****** {:s} - partial = {:b} - sparse = {:b}, {:s} *****".format(version, partial, sparse, smooth_method)
- print(param)
-
- sp1 = Spectral(version=version, partial=partial, sparse=sparse, smooth_method=smooth_method)
- start = timer()
- sp1 = sp1.fit(X)
- duration = timer() - start
- print("Unoptimized : " + str(duration))
-
-# sp2 = Spectral(version=version, partial=partial, sparse=sparse, smooth_method=smooth_method)
-# start = timer()
-# sp2 = sp2.fit(X)
-# duration = timer() - start
-# print("Optimized : " + str(duration))
-#
-# if sp1.hankel == sp2.hankel:
-# print("Same result.")
-# else:
-# print("The result is different", file=sys.stderr)
-
-def test_fit():
- adr = get_dataset_path("3.pautomac_light.train")
- data = load_data_sample(adr=adr)
- X = data.data
- param = {'version':['classic', 'prefix', 'suffix', 'factor'], 'partial':[True, False],
- 'sparse':[True, False], 'smooth_method':['none','trigram']}
- for version in param['version']:
-# for partial in param['partial']:
- for sparse in param['sparse']:
- for smooth_method in param['smooth_method']:
- launch_fit(X, version=version, partial=True, sparse=sparse, smooth_method=smooth_method)
-
-if __name__ == '__main__':
- test_fit()
diff --git a/examples/simple_automata.json b/examples/simple_automata.json
deleted file mode 100644
index ab271a879ea057c6064636ba019001158b41a863..0000000000000000000000000000000000000000
--- a/examples/simple_automata.json
+++ /dev/null
@@ -1 +0,0 @@
-{"automaton": {"final": {"numpy.ndarray": {"values": [0.15, 0.9], "dtype": "float64"}}, "initial": {"numpy.ndarray": {"values": [0.5, 0.8], "dtype": "float64"}}, "transitions": [{"numpy.ndarray": {"values": [[0.5, 0.15], [0.9, 0]], "dtype": "float64"}}, {"numpy.ndarray": {"values": [[0, 0], [0, -0.15]], "dtype": "float64"}}], "type": "classic", "nbS": 2, "nbL": 2}}
diff --git a/examples/simple_automata.json.gv b/examples/simple_automata.json.gv
deleted file mode 100644
index 0fd62e2d59b54c471ed4736cd2b9456c714a1897..0000000000000000000000000000000000000000
--- a/examples/simple_automata.json.gv
+++ /dev/null
@@ -1,15 +0,0 @@
-//Simple Automata
-digraph {
- 0 [label="0
-______
-> 0.50
-0.15 >"]
- 1 [label="1
-______
-> 0.80
-0.90 >"]
- 0 -> 0 [label="0:0.50"]
- 0 -> 1 [label="0:0.15"]
- 1 -> 0 [label="0:0.90"]
- 1 -> 1 [label="1:-0.15"]
-}
diff --git a/examples/simple_automata.json.gv.pdf b/examples/simple_automata.json.gv.pdf
deleted file mode 100644
index 9253c0b86d51eaa9a3f4014c4c4202bbf69c7aa4..0000000000000000000000000000000000000000
Binary files a/examples/simple_automata.json.gv.pdf and /dev/null differ
diff --git a/examples/simple_automata.yaml b/examples/simple_automata.yaml
deleted file mode 100644
index b85b6aec2fdc2ffe36ac8799948651b8c8c456ef..0000000000000000000000000000000000000000
--- a/examples/simple_automata.yaml
+++ /dev/null
@@ -1,23 +0,0 @@
-automaton:
- final:
- numpy.ndarray:
- dtype: float64
- values: [0.15, 0.9]
- initial:
- numpy.ndarray:
- dtype: float64
- values: [0.5, 0.8]
- nbL: 2
- nbS: 2
- transitions:
- - numpy.ndarray:
- dtype: float64
- values:
- - [0.5, 0.15]
- - [0.9, 0]
- - numpy.ndarray:
- dtype: float64
- values:
- - [0, 0]
- - [0, -0.15]
- type: classic
diff --git a/examples/simple_automata.yaml.gv b/examples/simple_automata.yaml.gv
deleted file mode 100644
index 0fd62e2d59b54c471ed4736cd2b9456c714a1897..0000000000000000000000000000000000000000
--- a/examples/simple_automata.yaml.gv
+++ /dev/null
@@ -1,15 +0,0 @@
-//Simple Automata
-digraph {
- 0 [label="0
-______
-> 0.50
-0.15 >"]
- 1 [label="1
-______
-> 0.80
-0.90 >"]
- 0 -> 0 [label="0:0.50"]
- 0 -> 1 [label="0:0.15"]
- 1 -> 0 [label="0:0.90"]
- 1 -> 1 [label="1:-0.15"]
-}
diff --git a/examples/simple_automata.yaml.gv.pdf b/examples/simple_automata.yaml.gv.pdf
deleted file mode 100644
index 9253c0b86d51eaa9a3f4014c4c4202bbf69c7aa4..0000000000000000000000000000000000000000
Binary files a/examples/simple_automata.yaml.gv.pdf and /dev/null differ
diff --git a/examples/svd_tests.py b/examples/svd_tests.py
deleted file mode 100644
index bcc28f24b03e6dcaa40a520ef3bd8155f664bdba..0000000000000000000000000000000000000000
--- a/examples/svd_tests.py
+++ /dev/null
@@ -1,101 +0,0 @@
-# -*- coding: utf-8 -*-
-
-'''
-Created on 20 févr. 2018
-
-@author: arrivault
-'''
-import sys
-from timeit import default_timer as timer
-import numpy as np
-
-def test_svd():
- d = 1000
- A = np.asarray(np.random.randint(1, 10,(d, d)), dtype=np.float32)
-
- # scipy
- print("\n****************** Scipy")
- from scipy.linalg import svd as sc_svd
- start = timer()
- u_sc, s_sc, v_sc = sc_svd(A)
- duration = timer() - start
- print("SVD scipy : " + str(duration))
- print(s_sc)
- print(type(s_sc))
-
- # cupy
- print("\n****************** Cupy")
- import cupy as cp
- from cupy.linalg import svd as cp_svd
- start = timer()
- A_cp = cp.asarray(A)
- u_cp, s_cp, v_cp = cp_svd(A_cp)
- u_cp, s_cp, v_cp = u_cp.get(), s_cp.get(), v_cp.get()
- duration = timer() - start
- print("SVD cupy : " + str(duration))
- print(s_cp)
- print(A_cp.device)
- print(type(s_cp))
-
- # scikit-cuda
- print("\n****************** scikit-cuda")
- import pycuda.autoinit
- import pycuda.gpuarray as gpuarray
- #import ctypes
- #ctypes.CDLL('libgomp.so.1', mode=ctypes.RTLD_GLOBAL)
- from skcuda import linalg as sk_linalg
- start = timer()
- sk_linalg.init()
- A_sk = gpuarray.to_gpu(A)
- u_sk, s_sk, vh_sk = sk_linalg.svd(A_sk, jobu='S', jobvt='S', lib='cusolver')
- u_sk, s_sk, v_sk = u_sk.get_async(), s_sk.get_async(), vh_sk.get_async()
- duration = timer() - start
- print("SVD skcuda : " + str(duration))
- print(s_sk)
- print(type(s_sk))
-
-# # tensorflow
-# print("\n****************** tensorflow")
-# import tensorflow as tf
-# start = timer()
-# with tf.device("/cpu:0"):
-# s_tf, u_tf, v_tf = tf.svd(A, compute_uv=True)
-# s_tf, u_tf, v_tf = tf.Session().run(s_tf), tf.Session().run(u_tf), tf.Session().run(v_tf)
-# duration = timer() - start
-# print("SVD tensorflow CPU : " + str(duration))
-# print(s_tf)
-# print(type(s_tf))
-# print()
-# start = timer()
-# with tf.device("/gpu:0"):
-# s_tf, u_tf, v_tf = tf.svd(A, compute_uv=True)
-# s_tf, u_tf, v_tf = tf.Session().run(s_tf), tf.Session().run(u_tf), tf.Session().run(v_tf)
-# duration = timer() - start
-# print("SVD tensorflow GPU : " + str(duration))
-# print(s_tf)
-# print(type(s_tf))
-
- # Theano
- print("\n****************** theano")
- from theano import function, shared, tensor, config
- import theano.tensor.nlinalg as th_linalg
- x = tensor.dmatrix('x')
- u, s, v = th_linalg.svd(x)
- f = function([x], (u, s, v))
- start = timer()
- u_th, s_th, v_th = f(A)
- duration = timer() - start
-
-# x = shared(np.asarray(A, config.floatX))
-# f = function([], th_linalg.svd(x))
-# start = timer()
-# u_th, s_th, v_th = f()
-# duration = timer() - start
-
- print("SVD theano : " + str(duration))
- print(s_th)
- print(type(s_th))
-
-
-if __name__ == '__main__':
- test_svd()
diff --git a/splearn/hankel.py b/splearn/hankel.py
index 24dc0cf6460e651415b87b0c69a1bbaed34e885f..133f85a98b341e5f0ea424ab798970e66bd90d3b 100644
--- a/splearn/hankel.py
+++ b/splearn/hankel.py
@@ -40,6 +40,8 @@ from __future__ import division, print_function
import scipy.sparse as sps
import scipy.sparse.linalg as lin
import numpy as np
+from numpy.linalg import svd, pinv
+from sklearn.utils.extmath import randomized_svd as sk_svd
class Hankel(object):
""" A Hankel instance , compute the list of Hankel matrices
@@ -61,6 +63,9 @@ class Hankel(object):
:param boolean partial: (default value = False) build of partial
:param boolean sparse: (default value = False) True if Hankel
matrix is sparse
+ :param boolean full_svd_calculation: (default value = False) if True the entire SVD is calculated
+ for building hankel matrix. Else it is done by the sklearn random algorithm only for the greatest
+ k=rank eigenvalues.
:param boolean mode_quiet: (default value = False) True for no
output message.
:param list lhankel: list of all Hankel matrices. At least one of the two parameters
@@ -86,11 +91,12 @@ class Hankel(object):
self, sample_instance=None,
lrows=[], lcolumns=[],
version="classic", partial=False,
- sparse=False, mode_quiet=False, lhankel=None):
+ sparse=False, full_svd_calculation=False, mode_quiet=False, lhankel=None):
self.version = version
self.partial = partial
self.sparse = sparse
+ self.full_svd__calculation = full_svd_calculation
self.build_from_sample = True
if sample_instance is not None:
# Size of the alphabet
@@ -363,24 +369,19 @@ class Hankel(object):
"the smaller dimension of the Hankel Matrix (" +
str(matrix_shape) + ")")
if not self.sparse:
- try:
- import theano.tensor as T
- from theano import function
- import theano.tensor.nlinalg as th_linalg
- x = T.dmatrix('x')
- u, s, v = th_linalg.svd(x)
- svd = function([x], (u, s, v))
- except :
- from scipy.linalg import svd
hankel = self.lhankel[0]
- [u, s, v] = svd(hankel)
- u = u[:, :rank]
- v = v[:rank, :]
- # ds = np.zeros((rank, rank), dtype=complex)
- ds = np.diag(s[:rank])
- pis = np.linalg.pinv(v)
+ if self.full_svd__calculation:
+ [u, s, v] = svd(hankel)
+ u = u[:, :rank]
+ v = v[:rank, :]
+ # ds = np.zeros((rank, rank), dtype=complex)
+ ds = np.diag(s[:rank])
+ else:
+ [u, s, v] = sk_svd(hankel, n_components=rank)
+ ds = np.diag(s)
+ pis = pinv(v)
del v
- pip = np.linalg.pinv(np.dot(u, ds))
+ pip = pinv(np.dot(u, ds))
del u, ds
init = np.dot(hankel[0, :], pis)
term = np.dot(pip, hankel[:, 0])
@@ -390,18 +391,25 @@ class Hankel(object):
trans.append(np.dot(pip, np.dot(hankel, pis)))
else:
- hankel = self.lhankel[0]
- [u, s, v] = lin.svds(hankel, k=rank)
+ hankel = self.lhankel[0].tocsr()
+ if self.full_svd__calculation:
+ [u, s, v] = svd(hankel.A)
+ u = u[:, :rank]
+ v = v[:rank, :]
+ # ds = np.zeros((rank, rank), dtype=complex)
+ ds = np.diag(s[:rank])
+ else:
+ [u, s, v] = lin.svds(hankel, k=rank)
ds = np.diag(s)
- pis = np.linalg.pinv(v)
+ pis = pinv(v)
del v
- pip = np.linalg.pinv(np.dot(u, ds))
+ pip = pinv(np.dot(u, ds))
del u, ds
init = hankel[0, :].dot(pis)[0, :]
term = np.dot(pip, hankel[:, 0].toarray())[:, 0]
trans = []
for x in range(self.nbL):
- hankel = self.lhankel[x+1]
+ hankel = self.lhankel[x+1].tocsr()
trans.append(np.dot(pip, hankel.dot(pis)))
A = Automaton(nbL=self.nbL, nbS=rank, initial=init, final=term,
diff --git a/splearn/spectral.py b/splearn/spectral.py
index e2b734139c34e3db52275da4bab340e0473b5c8d..ce705d2d3df8d0d0ffbb25640715f223e01e9b65 100644
--- a/splearn/spectral.py
+++ b/splearn/spectral.py
@@ -77,6 +77,9 @@ class Spectral(BaseEstimator):
of partial Hankel matrix
:param boolean sparse: (default value = False) True if Hankel
matrix is sparse
+ :param boolean full_svd_calculation: (default value = False) if True the entire SVD is calculated
+ for building hankel matrix. Else it is done by the sklearn random algorithm only for the greatest
+ k=rank eigenvalues.
:param string smooth_method: (default value = "none") method of smoothing
- 'trigram' the 3-Gram trigram dict
@@ -115,11 +118,12 @@ class Spectral(BaseEstimator):
"""
def __init__(self, rank=5, lrows=7, lcolumns=7,
version='classic', partial=True,
- sparse=True, smooth_method='none',
+ sparse=True, full_svd_calculation=False, smooth_method='none',
mode_quiet=False):
self.version = version
self.partial = partial
self.sparse = sparse
+ self.full_svd_calculation = full_svd_calculation
self.lrows = lrows
self.lcolumns = lcolumns
self.rank = rank
@@ -219,6 +223,7 @@ class Spectral(BaseEstimator):
lrows=self.lrows, lcolumns=self.lcolumns,
version=self.version,
partial=self.partial, sparse=self.sparse,
+ full_svd_calculation = self.full_svd_calculation,
mode_quiet=self.mode_quiet)
self._automaton = self._hankel.to_automaton(self.rank, self.mode_quiet)
# for smooth option compute trigram dictionnary
@@ -226,45 +231,8 @@ class Spectral(BaseEstimator):
self.trigram = self._threegramdict(X.sample)
return self
-
- def fit_opt(self, X, y=None):
- """Fit the model in a optimal way
-
- - Input:
-
- :param SplearnArray X: object of shape [n_samples,n_features]
- Training data
- :param ndarray y: (default value = None) not used by Spectral estimator
- numpy array of shape [n_samples] Target values
-
-
- - Output:
-
- :returns: Spectral itself with an automaton attribute instanced
- returns an instance of self.
- :rtype: Spectral
-
- """
- check_array(X)
- if not isinstance(X, SplearnArray):
- self._hankel = None
- self._automaton = None
- return self
- self.polulate_dictionnaries_opt(X)
- self._hankel = Hankel(sample_instance=X,
- lrows=self.lrows, lcolumns=self.lcolumns,
- version=self.version,
- partial=self.partial, sparse=self.sparse,
- mode_quiet=self.mode_quiet)
- self._automaton = self._hankel.to_automaton(self.rank, self.mode_quiet)
- # for smooth option compute trigram dictionnary
- if self.smooth == 1:
- self.trigram = self._threegramdict(X.sample)
-
- return self
-
- def polulate_dictionnaries_opt(self, X):
+ def polulate_dictionnaries(self, X):
"""Populates the *sample*, *pref*, *suff*, *fact* dictionnaries of X
- Input:
@@ -275,10 +243,10 @@ class Spectral(BaseEstimator):
"""
if not isinstance(X, SplearnArray):
return X
- X.sample = {} # dictionary (word,count)
- X.pref = {} # dictionary (prefix,count)
- X.suff = {} # dictionary (suffix,count)
- X.fact = {} # dictionary (factor,count)
+ dsample = {} # dictionary (word,count)
+ dpref = {} # dictionary (prefix,count)
+ dsuff = {} # dictionary (suffix,count)
+ dfact = {} # dictionary (factor,count)
if self.partial:
if isinstance(self.lrows, int):
lrowsmax = self.lrows
@@ -293,96 +261,7 @@ class Spectral(BaseEstimator):
lcolumnsmax = self.lcolumns.__len__()
version_columns_int = False
lmax = lrowsmax + lcolumnsmax
- #threads = []
- for line in range(X.shape[0]):
- self._populate_a_word(X, line, lrowsmax, version_rows_int,
- lcolumnsmax, version_columns_int, lmax)
- else:
- self._populate_a_word(X, line)
-
-
- if self.version == "classic":
- X.fact = {}
- elif self.version == "suffix":
- X.pref = {}
- elif self.version == "prefix":
- X.suff = {}
- elif self.version == "factor":
- X.suff = {}
- X.pref = {}
- def _populate_a_word(self, X, line, lrowsmax=None, version_rows_int=None,
- lcolumnsmax=None, version_columns_int=None, lmax=None):
- w = X[line, :]
- w = w[w >= 0]
- w = tuple([int(x) for x in w[0:]])
- X.sample[w] = X.sample.setdefault(w, 0) + 1
- if self.version == "prefix" or self.version == "classic":
- # empty word treatment for prefixe, suffix, and factor dictionnaries
- X.pref[()] = X.pref.setdefault((),0) + 1
- if self.version == "suffix" or self.version == "classic":
- X.suff[()] = X.suff.setdefault((),0) + 1
- if (self.version == "factor" or self.version == "suffix" or
- self.version == "prefix"):
- X.fact[()] = X.fact.setdefault((),0) + len(w) + 1
- if self.partial:
- for i in range(len(w)):
- if self.version == "classic":
- if ((version_rows_int and i + 1 <= lrowsmax) or
- (not version_rows_int and w[:i + 1] in self.lrows)):
- X.pref[w[:i + 1]] = X.pref.setdefault(w[:i + 1], 0) + 1
- if ((version_columns_int and i + 1 <= lcolumnsmax) or
- (not version_columns_int and w[-( i + 1):] in self.lcolumns)):
- X.suff[w[-(i + 1):]] = X.suff.setdefault(w[-(i + 1):], 0) + 1
- elif self.version == "prefix":
- # dictionaries dpref is populated until
- # lmax = lrows + lcolumns
- # dictionaries dfact is populated until lcolumns
- if (((version_rows_int or version_columns_int) and i + 1 <= lmax) or
- (not version_rows_int and w[:i + 1] in self.lrows) or
- (not version_columns_int and w[:i + 1] in self.lcolumns)):
- X.pref[w[:i + 1]] = X.pref.setdefault(w[:i + 1], 0) + 1
- for j in range(i + 1, len(w) + 1):
- if ((version_columns_int and (j - i) <= lmax) or
- (not version_columns_int and w[i:j] in self.lcolumns)):
- X.fact[w[i:j]] = X.fact.setdefault(w[i:j], 0) + 1
- elif self.version == "suffix":
- if (((version_rows_int or version_columns_int) and i <= lmax) or
- (not version_rows_int and w[-(i + 1):] in self.lrows) or
- (not version_columns_int and w[-(i + 1):] in self.lcolumns)):
- X.suff[w[-(i + 1):]] = X.suff.setdefault(w[-(i + 1):], 0) + 1
- for j in range(i + 1, len(w) + 1):
- if ((version_rows_int and (j - i) <= lmax) or
- (not version_rows_int and w[i:j] in self.lrows)):
- X.fact[w[i:j]] = X.fact.setdefault(w[i:j], 0) + 1
- elif self.version == "factor":
- for j in range(i + 1, len(w) + 1):
- if (((version_rows_int or version_columns_int) and (j - i) <= lmax) or
- (not version_rows_int and w[i:j] in self.lrows) or
- (not version_columns_int and w[i:j] in self.lcolumns)):
- X.fact[w[i:j]] = X.fact.setdefault(w[i:j], 0) + 1
- else: # not partial
- for i in range(len(w)):
- X.pref[w[:i + 1]] = X.pref.setdefault(w[:i + 1], 0) + 1
- X.suff[w[i:]] = X.suff.setdefault(w[i:], 0) + 1
- for j in range(i + 1, len(w) + 1):
- X.fact[w[i:j]] = X.fact.setdefault(w[i:j], 0) + 1
-
- def polulate_dictionnaries(self, X):
- """Populates the *sample*, *pref*, *suff*, *fact* dictionnaries of X
-
- - Input:
-
- :param SplearnArray X: object of shape [n_samples,n_features]
- Training data
-
- """
- if not isinstance(X, SplearnArray):
- return X
- dsample = {} # dictionary (word,count)
- dpref = {} # dictionary (prefix,count)
- dsuff = {} # dictionary (suffix,count)
- dfact = {} # dictionary (factor,count)
for line in range(X.shape[0]):
w = X[line, :]
w = w[w >= 0]
@@ -398,20 +277,6 @@ class Spectral(BaseEstimator):
dfact[()] = dfact[()] + len(w) + 1 if () in dfact else len(
w) + 1
if self.partial:
- if isinstance(self.lrows, int):
- lrowsmax = self.lrows
- version_rows_int = True
- else:
- version_rows_int = False
- lrowsmax = self.lrows.__len__()
- if isinstance(self.lcolumns, int):
- lcolumnsmax = self.lcolumns
- version_columns_int = True
- else:
- lcolumnsmax = self.lcolumns.__len__()
- version_columns_int = False
- lmax = lrowsmax + lcolumnsmax
-
for i in range(len(w)):
if self.version == "classic":
if (version_rows_int is True and