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row slice benchmark
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‎.ipynb_checkpoints/timeit_tests-checkpoint.ipynb‎

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{
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"metadata": {
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"name": "",
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"signature": "sha256:9e62469f6250ac6d58e6d3d2c67a5995d1505778b147ae918d71f4ebc1c12fb6"
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"signature": "sha256:8294645ddf3a6997a4764ca0bb61458953a6e579d858ecbf59e69882af05d0df"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"- [Dictionary operations](#dict_ops) \n",
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" - [Adding elements to a dictionary](#adding_dict_elements)\n",
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"- [Comprehensions vs. for-loops](#comprehensions)\n",
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"- [Copying files by searching directory trees](#find_copy)"
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"- [Copying files by searching directory trees](#find_copy)\n",
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"- [Returning column vectors slicing through a numpy array](#row_vectors)"
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]
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},
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{
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"prompt_number": 35
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [],
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"language": "python",
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"cell_type": "markdown",
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"metadata": {},
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"outputs": []
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"source": [
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"I have to say that I am really positively surprised. The shell's `find` scales even better than expected!"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"I have to say that I am really positively surprised. The shell's `find` scales even better than expected!"
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"<br>\n",
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"<br>\n",
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"<a name='row_vectors'></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Returning column vectors slicing through a numpy array"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Given a numpy matrix, I want to iterate through it and return each column as a 1-column vector. \n",
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"E.g., if I want to return the 1st column from matrix A below\n",
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"\n",
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"<pre>\n",
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"A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])\n",
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">>> A\n",
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"array([[1, 2, 3],\n",
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" [4, 5, 6],\n",
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" [7, 8, 9]])</pre>\n",
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"\n",
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"I want my result to be:\n",
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"<pre>\n",
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"array([[1],\n",
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" [4],\n",
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" [7]])</pre>\n",
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"\n",
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"with `.shape` = `(3,1)`\n",
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"\n",
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"\n",
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"However, the default behavior of numpy is to return the column as a row vector:\n",
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"\n",
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"<pre>\n",
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">>> A[:,0]\n",
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"array([1, 4, 7])\n",
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">>> A[:,0].shape\n",
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"(3,)\n",
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"</pre>"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import numpy as np\n",
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"\n",
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"def colvec_method1(A):\n",
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" for col in A.T:\n",
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" colvec = row[:,np.newaxis]\n",
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" yield colvec"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 83
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"def colvec_method2(A):\n",
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" for idx in range(A.shape[1]):\n",
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" colvec = A[:,idx:idx+1]\n",
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" yield colvec"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 82
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"def colvec_method3(A):\n",
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" for idx in range(A.shape[1]):\n",
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" colvec = A[:,idx].reshape(A.shape[0],1)\n",
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" yield colvec"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 81
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"def colvec_method4(A):\n",
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" for idx in range(A.shape[1]):\n",
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" colvec = np.vstack(A[:,idx])\n",
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" yield colvec"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 79
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"def colvec_method5(A):\n",
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" for idx in range(A.shape[1]):\n",
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" colvec = np.row_stack(A[:,idx])\n",
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" yield colvec"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 77
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"def colvec_method6(A):\n",
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" for idx in range(A.shape[1]):\n",
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" colvec = np.column_stack((A[:,idx],))\n",
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" yield colvec"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 74
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"def test_method(method, A):\n",
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" for i in method(A): \n",
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" assert i.shape == (A.shape[0],1), \"{}, {}\".format(i.shape, A.shape[0],1)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 69
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import timeit\n",
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"\n",
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"A = np.random.random((300, 3))\n",
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"\n",
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"for method in [\n",
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" colvec_method1, colvec_method2, \n",
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" colvec_method3, colvec_method4, \n",
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" colvec_method5, colvec_method6]:\n",
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" print('\\nTest:', method.__name__)\n",
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" %timeit test_method(colvec_method2, A)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"\n",
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"Test: colvec_method1\n",
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"100000 loops, best of 3: 17.7 \u00b5s per loop"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"\n",
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"\n",
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"Test: colvec_method2\n",
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"10000 loops, best of 3: 16.4 \u00b5s per loop"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"\n",
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"\n",
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"Test: colvec_method3\n",
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"100000 loops, best of 3: 17.3 \u00b5s per loop"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"\n",
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"\n",
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"Test: colvec_method4\n",
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"10000 loops, best of 3: 16.4 \u00b5s per loop"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"\n",
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"\n",
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"Test: colvec_method5\n",
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"100000 loops, best of 3: 17.1 \u00b5s per loop"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"\n",
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"\n",
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"Test: colvec_method6\n",
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"100000 loops, best of 3: 16.6 \u00b5s per loop"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"\n"
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]
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}
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],
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"prompt_number": 86
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},
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{
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"cell_type": "code",
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"collapsed": false,

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