--- a
+++ b/Activity Detection.ipynb
@@ -0,0 +1,266 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "%matplotlib inline\n",
+    "plt.style.use('ggplot')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def read_data(file_path):\n",
+    "    column_names = ['user-id','activity','timestamp', 'x-axis', 'y-axis', 'z-axis']\n",
+    "    data = pd.read_csv(file_path,header = None, names = column_names)\n",
+    "    return data\n",
+    "\n",
+    "def feature_normalize(dataset):\n",
+    "    mu = np.mean(dataset,axis = 0)\n",
+    "    sigma = np.std(dataset,axis = 0)\n",
+    "    return (dataset - mu)/sigma\n",
+    "    \n",
+    "def plot_axis(ax, x, y, title):\n",
+    "    ax.plot(x, y)\n",
+    "    ax.set_title(title)\n",
+    "    ax.xaxis.set_visible(False)\n",
+    "    ax.set_ylim([min(y) - np.std(y), max(y) + np.std(y)])\n",
+    "    ax.set_xlim([min(x), max(x)])\n",
+    "    ax.grid(True)\n",
+    "    \n",
+    "def plot_activity(activity,data):\n",
+    "    fig, (ax0, ax1, ax2) = plt.subplots(nrows = 3, figsize = (15, 10), sharex = True)\n",
+    "    plot_axis(ax0, data['timestamp'], data['x-axis'], 'x-axis')\n",
+    "    plot_axis(ax1, data['timestamp'], data['y-axis'], 'y-axis')\n",
+    "    plot_axis(ax2, data['timestamp'], data['z-axis'], 'z-axis')\n",
+    "    plt.subplots_adjust(hspace=0.2)\n",
+    "    fig.suptitle(activity)\n",
+    "    plt.subplots_adjust(top=0.90)\n",
+    "    plt.show()\n",
+    "    \n",
+    "def windows(data, size):\n",
+    "    start = 0\n",
+    "    while start < data.count():\n",
+    "        yield int(start), int(start + size)\n",
+    "        start += (size / 2)\n",
+    "\n",
+    "def segment_signal(data,window_size = 90):\n",
+    "    segments = np.empty((0,window_size,3))\n",
+    "    labels = np.empty((0))\n",
+    "    for (start, end) in windows(data['timestamp'], window_size):\n",
+    "        x = data[\"x-axis\"][start:end]\n",
+    "        y = data[\"y-axis\"][start:end]\n",
+    "        z = data[\"z-axis\"][start:end]\n",
+    "        if(len(dataset['timestamp'][start:end]) == window_size):\n",
+    "            segments = np.vstack([segments,np.dstack([x,y,z])])\n",
+    "            labels = np.append(labels,stats.mode(data[\"activity\"][start:end])[0][0])\n",
+    "    return segments, labels\n",
+    "\n",
+    "def weight_variable(shape):\n",
+    "    initial = tf.truncated_normal(shape, stddev = 0.1)\n",
+    "    return tf.Variable(initial)\n",
+    "\n",
+    "def bias_variable(shape):\n",
+    "    initial = tf.constant(0.0, shape = shape)\n",
+    "    return tf.Variable(initial)\n",
+    "\n",
+    "def depthwise_conv2d(x, W):\n",
+    "    return tf.nn.depthwise_conv2d(x,W, [1, 1, 1, 1], padding='VALID')\n",
+    "\n",
+    "def apply_depthwise_conv(x,kernel_size,num_channels,depth):\n",
+    "    weights = weight_variable([1, kernel_size, num_channels, depth])\n",
+    "    biases = bias_variable([depth * num_channels])\n",
+    "    return tf.nn.relu(tf.add(depthwise_conv2d(x, weights),biases))\n",
+    "    \n",
+    "def apply_max_pool(x,kernel_size,stride_size):\n",
+    "    return tf.nn.max_pool(x, ksize=[1, 1, kernel_size, 1], \n",
+    "                          strides=[1, 1, stride_size, 1], padding='VALID')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dataset = read_data('actitracker_raw.txt')\n",
+    "dataset['x-axis'] = feature_normalize(dataset['x-axis'])\n",
+    "dataset['y-axis'] = feature_normalize(dataset['y-axis'])\n",
+    "dataset['z-axis'] = feature_normalize(dataset['z-axis'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "for activity in np.unique(dataset[\"activity\"]):\n",
+    "    subset = dataset[dataset[\"activity\"] == activity][:180]\n",
+    "    plot_activity(activity,subset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "segments, labels = segment_signal(dataset)\n",
+    "labels = np.asarray(pd.get_dummies(labels), dtype = np.int8)\n",
+    "reshaped_segments = segments.reshape(len(segments), 1,90, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "train_test_split = np.random.rand(len(reshaped_segments)) < 0.70\n",
+    "train_x = reshaped_segments[train_test_split]\n",
+    "train_y = labels[train_test_split]\n",
+    "test_x = reshaped_segments[~train_test_split]\n",
+    "test_y = labels[~train_test_split]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "input_height = 1\n",
+    "input_width = 90\n",
+    "num_labels = 6\n",
+    "num_channels = 3\n",
+    "\n",
+    "batch_size = 10\n",
+    "kernel_size = 60\n",
+    "depth = 60\n",
+    "num_hidden = 1000\n",
+    "\n",
+    "learning_rate = 0.0001\n",
+    "training_epochs = 8\n",
+    "\n",
+    "total_batches = train_x.shape[0] // batch_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "X = tf.placeholder(tf.float32, shape=[None,input_height,input_width,num_channels])\n",
+    "Y = tf.placeholder(tf.float32, shape=[None,num_labels])\n",
+    "\n",
+    "c = apply_depthwise_conv(X,kernel_size,num_channels,depth)\n",
+    "p = apply_max_pool(c,20,2)\n",
+    "c = apply_depthwise_conv(p,6,depth*num_channels,depth//10)\n",
+    "\n",
+    "shape = c.get_shape().as_list()\n",
+    "c_flat = tf.reshape(c, [-1, shape[1] * shape[2] * shape[3]])\n",
+    "\n",
+    "f_weights_l1 = weight_variable([shape[1] * shape[2] * depth * num_channels * (depth//10), num_hidden])\n",
+    "f_biases_l1 = bias_variable([num_hidden])\n",
+    "f = tf.nn.tanh(tf.add(tf.matmul(c_flat, f_weights_l1),f_biases_l1))\n",
+    "\n",
+    "out_weights = weight_variable([num_hidden, num_labels])\n",
+    "out_biases = bias_variable([num_labels])\n",
+    "y_ = tf.nn.softmax(tf.matmul(f, out_weights) + out_biases)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "loss = -tf.reduce_sum(Y * tf.log(y_))\n",
+    "optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(loss)\n",
+    "\n",
+    "correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1))\n",
+    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "cost_history = np.empty(shape=[1],dtype=float)\n",
+    "\n",
+    "with tf.Session() as session:\n",
+    "    tf.global_variables_initializer().run()\n",
+    "    for epoch in range(training_epochs):\n",
+    "        for b in range(total_batches):    \n",
+    "            offset = (b * batch_size) % (train_y.shape[0] - batch_size)\n",
+    "            batch_x = train_x[offset:(offset + batch_size), :, :, :]\n",
+    "            batch_y = train_y[offset:(offset + batch_size), :]\n",
+    "            _, c = session.run([optimizer, loss],feed_dict={X: batch_x, Y : batch_y})\n",
+    "            cost_history = np.append(cost_history,c)\n",
+    "        print \"Epoch: \",epoch,\" Training Loss: \",c,\" Training Accuracy: \",\n",
+    "              session.run(accuracy, feed_dict={X: train_x, Y: train_y})\n",
+    "    \n",
+    "    print \"Testing Accuracy:\", session.run(accuracy, feed_dict={X: test_x, Y: test_y})"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [conda root]",
+   "language": "python",
+   "name": "conda-root-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}