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test
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‎joint_control/learn_posture.ipynb

Lines changed: 77 additions & 110 deletions
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@@ -37,25 +37,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 128,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING: pylab import has clobbered these variables: ['clf', 'permutation']\n",
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"`%matplotlib` prevents importing * from pylab and numpy\n"
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]
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}
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],
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"source": [
@@ -70,10 +60,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 129,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
@@ -90,10 +78,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 130,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_pose_data(i):\n",
@@ -109,17 +95,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 142,
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"execution_count": 4,
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"metadata": {
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"collapsed": false,
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"total number of data 221\n"
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"total number of data 222\n"
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]
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}
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],
@@ -139,10 +124,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 132,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# shuffule data\n",
@@ -162,10 +145,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 133,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"clf = svm.SVC(gamma=0.001, C=100.)"
@@ -180,20 +161,19 @@
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},
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{
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"cell_type": "code",
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"execution_count": 134,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,\n",
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" gamma=0.001, kernel='rbf', max_iter=-1, probability=False,\n",
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" random_state=None, shrinking=True, tol=0.001, verbose=False)"
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"SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0,\n",
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" decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
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" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
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" tol=0.001, verbose=False)"
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]
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},
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"execution_count": 134,
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -212,32 +192,28 @@
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},
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{
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"cell_type": "code",
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"execution_count": 135,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(array([10]), 10)"
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"(array([10]), array([10]))"
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]
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},
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"execution_count": 135,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"clf.predict(all_data[-1]), all_target[-1]"
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"clf.predict(all_data[-1:]), all_target[-1:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 136,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"def evaluate(expected, predicted):\n",
@@ -248,10 +224,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 137,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
@@ -260,33 +234,33 @@
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"Classification report:\n",
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" precision recall f1-score support\n",
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"\n",
263-
" 0 1.00 1.00 1.00 7\n",
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" 0 1.00 1.00 1.00 5\n",
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" 1 1.00 1.00 1.00 7\n",
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" 2 1.00 1.00 1.00 15\n",
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" 3 1.00 1.00 1.00 5\n",
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" 2 1.00 1.00 1.00 16\n",
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" 3 1.00 1.00 1.00 7\n",
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" 4 0.95 1.00 0.97 18\n",
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" 5 1.00 1.00 1.00 14\n",
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" 6 1.00 1.00 1.00 15\n",
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" 7 1.00 0.86 0.92 7\n",
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" 5 1.00 1.00 1.00 13\n",
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" 6 1.00 1.00 1.00 13\n",
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" 7 1.00 0.80 0.89 5\n",
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" 8 1.00 1.00 1.00 19\n",
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" 9 1.00 1.00 1.00 6\n",
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" 10 1.00 1.00 1.00 41\n",
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" 9 1.00 1.00 1.00 10\n",
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" 10 1.00 1.00 1.00 42\n",
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"\n",
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"avg / total 0.99 0.99 0.99 154\n",
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"avg / total 0.99 0.99 0.99 155\n",
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"\n",
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"\n",
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"Confusion matrix:\n",
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"[[ 7 0 0 0 0 0 0 0 0 0 0]\n",
253+
"[[ 5 0 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 7 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 15 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 5 0 0 0 0 0 0 0]\n",
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" [ 0 0 16 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 7 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 18 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 14 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 15 0 0 0 0]\n",
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" [ 0 0 0 0 1 0 0 6 0 0 0]\n",
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" [ 0 0 0 0 0 13 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 13 0 0 0 0]\n",
260+
" [ 0 0 0 0 1 0 0 4 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 19 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 6 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0 41]]\n"
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" [ 0 0 0 0 0 0 0 0 0 10 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0 42]]\n"
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]
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}
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],
@@ -308,10 +282,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 138,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
@@ -320,33 +292,33 @@
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"Classification report:\n",
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" precision recall f1-score support\n",
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"\n",
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" 0 1.00 1.00 1.00 3\n",
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" 0 1.00 1.00 1.00 5\n",
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" 1 1.00 1.00 1.00 3\n",
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" 2 1.00 1.00 1.00 5\n",
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" 3 1.00 1.00 1.00 5\n",
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" 2 1.00 1.00 1.00 4\n",
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" 3 1.00 1.00 1.00 3\n",
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" 4 1.00 1.00 1.00 12\n",
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" 5 1.00 1.00 1.00 8\n",
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" 6 1.00 1.00 1.00 4\n",
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" 7 1.00 1.00 1.00 4\n",
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" 8 1.00 0.86 0.92 7\n",
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" 9 1.00 1.00 1.00 5\n",
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" 10 0.92 1.00 0.96 11\n",
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" 5 1.00 1.00 1.00 10\n",
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" 6 1.00 1.00 1.00 6\n",
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" 7 1.00 1.00 1.00 6\n",
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" 8 1.00 1.00 1.00 7\n",
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" 9 1.00 1.00 1.00 1\n",
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" 10 1.00 1.00 1.00 10\n",
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"\n",
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"avg / total 0.99 0.99 0.98 67\n",
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"avg / total 1.00 1.00 1.00 67\n",
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"\n",
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"\n",
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"Confusion matrix:\n",
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"[[ 3 0 0 0 0 0 0 0 0 0 0]\n",
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"[[ 5 0 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 3 0 0 0 0 0 0 0 0 0]\n",
341-
" [ 0 0 5 0 0 0 0 0 0 0 0]\n",
342-
" [ 0 0 0 5 0 0 0 0 0 0 0]\n",
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" [ 0 0 4 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 3 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 12 0 0 0 0 0 0]\n",
344-
" [ 0 0 0 0 0 8 0 0 0 0 0]\n",
345-
" [ 0 0 0 0 0 0 4 0 0 0 0]\n",
346-
" [ 0 0 0 0 0 0 0 4 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 6 0 1]\n",
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" [ 0 0 0 0 0 0 0 0 0 5 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0 11]]\n"
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" [ 0 0 0 0 0 10 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 6 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 6 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 7 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 1 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0 10]]\n"
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]
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}
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],
@@ -371,10 +343,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 139,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pickle\n",
@@ -391,34 +361,31 @@
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},
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{
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"cell_type": "code",
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"execution_count": 140,
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"execution_count": 15,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(array([10]), 10)"
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"(array([10]), array([10]))"
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]
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},
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"execution_count": 140,
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"clf2 = pickle.load(open(ROBOT_POSE_CLF))\n",
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"clf2.predict(all_data[-1]), all_target[-1]"
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"clf2.predict(all_data[-1:]), all_target[-1:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 141,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
@@ -433,7 +400,7 @@
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"'Back'"
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]
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},
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"execution_count": 141,
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -442,7 +409,7 @@
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"test_data = [0] * len(all_data[-1])\n",
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"test_data[-1] = -1.5\n",
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"print test_data\n",
445-
"classes[clf2.predict(test_data)[0]]"
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"classes[clf2.predict([test_data])[0]]"
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]
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},
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{
@@ -488,5 +455,5 @@
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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"nbformat_minor": 1
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}

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