diff --git a/examples/PythonOptimizations.ipynb b/examples/PythonOptimizations.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..386c4d339a122e7cad01df2a0be4a8152f21b165
--- /dev/null
+++ b/examples/PythonOptimizations.ipynb
@@ -0,0 +1,394 @@
+{
+ "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",
+ " -4.00247842e-01 1.86116919e-02 2.44872691e-03 3.97552028e-02\n",
+ " -3.61032963e+00 -5.08877039e-01 3.09200644e-01 -2.52947181e-01\n",
+ " -1.45006254e-01 -2.06694026e-02 -6.71102941e-01 -2.49164805e-01\n",
+ " -2.45076999e-01 -8.83047581e-02 -2.77056009e-01 -3.63603652e-01\n",
+ " 9.13712531e-02 1.50326788e-01 -1.89215928e-01 6.49247885e-01\n",
+ " -1.04680789e+00 4.51940441e+00 -4.10190582e-01 -1.03717482e+00\n",
+ " -2.83955693e-01 2.13240623e-01 -4.06043559e-01 1.64782095e+00\n",
+ " 3.88930738e-02 1.79433972e-01 -1.63999215e-01 8.21388125e-01\n",
+ " 3.96741897e-01 -1.78203881e+00 -7.89099187e-02 -2.08030403e-01\n",
+ " -1.67499959e+00 -6.03813231e-02 9.04122651e-01 2.57910769e-02\n",
+ " -2.13610873e-01 1.05494165e+00 1.07709356e-02 -4.04108614e-02\n",
+ " -1.21986246e+00 3.52327973e-01 9.47596207e-02 -8.17048013e-01\n",
+ " 4.55845296e-01 1.14165060e-01 -8.36074054e-01 -2.73001790e+00\n",
+ " -2.25733072e-02 1.01752472e+00 1.58793926e-01 -3.07355216e-03\n",
+ " 3.39909703e-01 3.54566932e-01 4.76458110e-03 -2.74281472e-01\n",
+ " -4.90250558e-01 -5.93070447e-01 4.16583568e-01 1.40949559e+00\n",
+ " -6.85005546e-01 5.71568191e-01 2.63672054e-01 -2.65214413e-01\n",
+ " -1.82554388e+00 -2.56274521e-01 -2.20750004e-01 2.96699733e-01\n",
+ " -6.49191618e-01 -4.23787624e-01 1.40397280e-01 5.71154011e-03\n",
+ " -1.53047368e-01 -1.18402898e+00 8.88164192e-02 -7.80049741e-01\n",
+ " 1.22469738e-01 5.82156956e-01 -2.78263122e-01 5.70335388e+00\n",
+ " 2.33998346e+00 9.56273079e-01 1.51689303e+00 -4.10727739e-01\n",
+ " 4.94410247e-01 -3.66233498e-01 7.14528918e-01 2.38090664e-01\n",
+ " -6.46302760e-01 -3.08008194e-01 -3.49969387e-01 1.53435739e-02\n",
+ " 5.77413812e-02 3.72533381e-01 1.98260975e+00 -7.76899280e-03\n",
+ " -1.54382363e-01 -1.86449081e-01 -9.56342891e-02 1.03035802e-02\n",
+ " 1.14140980e-01 1.41981363e+00 -7.38705873e-01 -2.08745885e+00\n",
+ " -6.28826141e-01 1.02032393e-01 -7.70327747e-01 5.62456894e+00\n",
+ " -2.95871317e-01 1.26501453e+00 4.17780161e-01 8.68652642e-01\n",
+ " -4.87591237e-01 6.19750731e-02 -1.26040816e+00 -3.43194783e-01\n",
+ " 1.27273810e+00 -3.55367869e-01 2.32648347e-02 2.89628506e-01\n",
+ " -1.51091516e-01 2.83231330e-03 -1.51034407e-02 1.76298320e+00\n",
+ " -3.59570235e-01 2.01595259e+00 -7.87402838e-02 4.81955826e-01\n",
+ " -1.40068781e+00 2.83799553e-03 6.27174973e-01 3.14519048e-01\n",
+ " -1.10712957e+00 -3.53843659e-01 -6.35562837e-01 4.04969931e-01\n",
+ " -1.65314469e-02 4.08839643e-01 8.27084303e-01 3.12124312e-01\n",
+ " -4.19728532e-02 -4.32954282e-01 5.92173636e-01 -1.95017815e+00\n",
+ " 3.28963660e-02 6.78022718e-03 -1.16085088e+00 5.09182066e-02\n",
+ " -6.12896085e-01 -8.15577134e-02 1.12161350e+00 -3.40709865e-01\n",
+ " 2.52548099e-01 -4.65507209e-01 5.91823518e-01 -7.46792078e-01\n",
+ " 1.19965337e-01 6.05180681e-01 -5.09674013e-01 2.71683186e-01\n",
+ " 2.30722860e-01 -1.65039837e-01 -8.05439278e-02 1.71006903e-01\n",
+ " -2.81729937e-01 1.10326950e-02 3.20048153e-01 9.83216614e-03\n",
+ " 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
index 9eb6d59da95bada73e78a0bfc48006389eaa4f44..0c2987e147fb22496d3a38968b4565f1b82e044d 100644
--- a/examples/performances_calculation.py
+++ b/examples/performances_calculation.py
@@ -12,26 +12,38 @@ from splearn import Spectral
from splearn.tests.datasets.get_dataset_path import get_dataset_path
from splearn.datasets.base import load_data_sample
-def test():
- adr = get_dataset_path("3.pautomac.train")
- data = load_data_sample(adr=adr)
- X = data.data
- sp1 = Spectral()
+def launch(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("Classic : " + str(duration))
-
- sp2 = Spectral()
- start = timer()
- sp2 = sp2.fit_opt(X)
- duration = timer() - start
- print("Opt : " + str(duration))
-
- if sp1.hankel == sp2.hankel:
- print("Same result.")
- else:
- print("The result is different", file=sys.stderr)
+ 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():
+ 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(X, version=version, partial=True, sparse=sparse, smooth_method=smooth_method)
if __name__ == '__main__':
test()
diff --git a/splearn/hankel.py b/splearn/hankel.py
index 627203eb19c9116da30ee6f903faec3f7a8fe87c..7318d05dc0fac23c531f7a04a9aab9f4af83e70b 100644
--- a/splearn/hankel.py
+++ b/splearn/hankel.py
@@ -115,33 +115,33 @@ class Hankel(object):
def __eq__(self, other):
#print("Hankel equality check")
if self.version != other.version:
- #print("version is different")
+ # print("version is different")
return False
if self.partial != other.partial:
- #print("partial is different")
+ # print("partial is different")
return False
if self.sparse != other.sparse:
- #print("sparse is different")
+ # print("sparse is different")
return False
if self.build_from_sample != other.build_from_sample:
- #print("build_from_sample is different")
+ # print("build_from_sample is different")
return False
if self.nbL != other.nbL:
- #print("nbL is different")
+ # print("nbL is different")
return False
if self.nbEx != other.nbEx:
- #print("nbEx is different")
+ # print("nbEx is different")
return False
if len(self.lhankel) != len(other.lhankel):
- #print("lhankel length is different")
+ # print("lhankel length is different")
return False
for lh1, lh2 in zip(self.lhankel, other.lhankel):
if self.sparse:
if (lh1 != lh2).nnz > 0:
- #print("{:d} elements oh lhandel are different".format((lh1 != lh2).nnz))
+ # print("{:d} elements oh lhandel are different".format((lh1 != lh2).nnz))
return False
elif not np.array_equal(lh1, lh2):
- #print("Different Array")
+ # print("Different Array")
return False
return True
diff --git a/splearn/spectral.py b/splearn/spectral.py
index 3ff0e4e57b09a54816101ddf3830e9694d6b37ce..e2b734139c34e3db52275da4bab340e0473b5c8d 100644
--- a/splearn/spectral.py
+++ b/splearn/spectral.py
@@ -222,7 +222,7 @@ class Spectral(BaseEstimator):
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:
+ if self.smooth:
self.trigram = self._threegramdict(X.sample)
return self
@@ -251,8 +251,7 @@ class Spectral(BaseEstimator):
self._hankel = None
self._automaton = None
return self
- #self.polulate_dictionnaries_opt(X)
- self.polulate_dictionnaries_async(X)
+ self.polulate_dictionnaries_opt(X)
self._hankel = Hankel(sample_instance=X,
lrows=self.lrows, lcolumns=self.lcolumns,
version=self.version,
@@ -280,8 +279,6 @@ class Spectral(BaseEstimator):
X.pref = {} # dictionary (prefix,count)
X.suff = {} # dictionary (suffix,count)
X.fact = {} # dictionary (factor,count)
- futures = []
- pool = ThreadPoolExecutor(1)
if self.partial:
if isinstance(self.lrows, int):
lrowsmax = self.lrows
@@ -298,89 +295,21 @@ class Spectral(BaseEstimator):
lmax = lrowsmax + lcolumnsmax
#threads = []
for line in range(X.shape[0]):
- futures.append(pool.submit(self._populate_a_word, X, line, lrowsmax, version_rows_int,
- lcolumnsmax, version_columns_int, lmax))
-# self._populate_a_word(X, line, lrowsmax, version_rows_int,
-# lcolumnsmax, version_columns_int, lmax)
-# )
-# threads.append(threading.Thread(target = self._populate_a_word,
-# args=(X, line, lrowsmax, version_rows_int,
-# lcolumnsmax, version_columns_int, lmax)
-# ).start())
+ self._populate_a_word(X, line, lrowsmax, version_rows_int,
+ lcolumnsmax, version_columns_int, lmax)
else:
- for line in range(X.shape[0]):
- futures.append(pool.submit(self._populate_a_word, X, line))
-# self._populate_a_word(X, line)
- wait(futures)
+ self._populate_a_word(X, line)
- def _populate_a_word_locked(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
- with lock:
- X.pref[()] = X.pref.setdefault((),0) + 1
- if self.version == "suffix" or self.version == "classic":
- with lock:
- X.suff[()] = X.suff.setdefault((),0) + 1
- if (self.version == "factor" or self.version == "suffix" or
- self.version == "prefix"):
- with lock:
- 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)):
- with lock:
- 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)):
- with lock:
- 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)):
- with lock:
- 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)):
- with lock:
- 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)):
- with lock:
- 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)):
- with lock:
- 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)):
- with lock:
- X.fact[w[i:j]] = X.fact.setdefault(w[i:j], 0) + 1
- else: # not partial
- for i in range(len(w)):
- with lock:
- 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):
- with lock:
- X.fact[w[i:j]] = X.fact.setdefault(w[i:j], 0) + 1
+
+ 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):
@@ -439,137 +368,6 @@ class Spectral(BaseEstimator):
for j in range(i + 1, len(w) + 1):
X.fact[w[i:j]] = X.fact.setdefault(w[i:j], 0) + 1
- def _populate_generator(self, X, lrowsmax=None, version_rows_int=None,
- lcolumnsmax=None, version_columns_int=None, lmax=None):
- for line in range(X.shape[0]):
- w = X[line, :]
- w = w[w >= 0]
- w = tuple([int(x) for x in w[0:]])
- yield ('sample', w, 0)
- if self.version == "prefix" or self.version == "classic":
- # empty word treatment for prefixe, suffix, and factor dictionnaries
- yield ('pref', (), 0)
- if self.version == "suffix" or self.version == "classic":
- yield ('suff', (), 0)
- if (self.version == "factor" or self.version == "suffix" or
- self.version == "prefix"):
- yield ('fact', (), len(w))
- 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)):
- yield ('pref', w[:i + 1], 0)
- if ((version_columns_int and i + 1 <= lcolumnsmax) or
- (not version_columns_int and w[-( i + 1):] in self.lcolumns)):
- yield ('suff', w[-(i + 1):], 0)
- 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)):
- yield ('pref', w[:i + 1], 0)
- 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)):
- yield ('fact', w[i:j], 0)
- 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)):
- yield ('suff', w[-(i + 1):], 0)
- 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)):
- yield ('fact', w[i:j], 0)
- 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)):
- yield ('fact', w[i:j], 0)
- else: # not partial
- for i in range(len(w)):
- yield ('pref', w[:i + 1], 0)
- yield ('suff', w[i:], 0)
- for j in range(i + 1, len(w) + 1):
- yield ('fact', w[i:j], 0)
-
- def _populate_coroutine(self, d):
- print("Ready to populate")
- while True:
- key, val = (yield)
- d[key] = d.setdefault(key, 0) + val + 1
-
- def _populate_each_value(self, d, s, key, val):
- if val:
- d[key] = d.setdefault(key, 0) + val + 1
- else:
- d[key] = d.setdefault(key, 0) + 1
-
- def polulate_dictionnaries_async(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
- X.sample = {} # dictionary (word,count)
- X.pref = {} # dictionary (prefix,count)
- X.suff = {} # dictionary (suffix,count)
- X.fact = {} # dictionary (factor,count)
- rsample = self._populate_coroutine(X.sample)
- next(rsample)
- rpref = self._populate_coroutine(X.pref)
- next(rpref)
- rsuff = self._populate_coroutine(X.suff)
- next(rsuff)
- rfact = self._populate_coroutine(X.fact)
- next(rfact)
- 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 s, key, val in self._populate_generator(X, lrowsmax, version_rows_int, lcolumnsmax, version_columns_int, lmax):
-# d = getattr(X, s)
-# self._populate_each_value(d, s, key, val)
- if s == 'fact':
- rfact.send((key, val))
- elif s == 'pref':
- rpref.send((key, val))
- elif s == 'suff':
- rsuff.send((key, val))
- else:
- rsample.send((key, val))
- else:
- for s, key, val in self._populate_generator(X):
-# d = getattr(X, s)
-# self._populate_each_value(d, s, key, val)
- if s == 'fact':
- rfact.send((key, val))
- elif s == 'pref':
- rpref.send((key, val))
- elif s == 'suff':
- rsuff.send((key, val))
- else:
- rsample.send((key, val))
-
def polulate_dictionnaries(self, X):
"""Populates the *sample*, *pref*, *suff*, *fact* dictionnaries of X