A repository for the combined code of Muvr that would be used during Datapalooza.

muvr is a demonstration of an application that uses wearable devices (Pebble)—in combination with a mobile app—to submit physical (i.e. accelerometer, compass) and biological (i.e. heart rate) information to a CQRS/ES cluster to be analysed.

• Install Cocoapods (latest 0.39.0)

  1. If using HomeBrew brew install Caskroom/cask/cocoapods
  2. sudo gem install cocoapods

  Note: Cocoapods needs Ruby in your system

• Install the pod dependencies by running pod install

• Load Muvr.xcworkspace on Xcode

• [Optional] Remove Pods.Framework in Xcode dependencies

• Build and run

Our sensor data is fed in the the classifier in windows 400 samples long. Each sample contains acceleration along the x, y, and z axes. The MLP we trained recognises three exercises: biceps curl, triceps extension, and lateral raise. Its hidden layers have 250 and 100 perceptrons.

• Run the playground and classify the exercise data in the variable sd.
• explore the MKClassifier initializer
• reason about the sizes of the first and last layer
• implement the tanh and sigmoid activation functions
• call the appropriate function on the MKClassifier instance

Using loops to implement the activation functions is not efficient. Our code should make the most of the vector and DSP hardware in modern mobiles.

Instead of for var i = 0; i < in.count; ++i { in[i] = tanh(in[i]) }, use vectlib and vDSP functions. In this example, use the vvtanhf function for tanh. For sigmoid, find and use functions that perform the following in-place operations:

• x ← -x
• x ← e^x
• x ← x + const; in our case x + 1
• x ← x^const; in our case x^-1

use these functions in a better implementation of tanh and sigmoid AFs.

cd analytics
./init-env.sh

To start development on Python code or notebooks

source venv/bin/activate
# After you call this line, you should see (venv) added at the beginning of your shell prompt. 

• In mlp/training/mlp_model.py, construct the inner layers for the MLP
• Recall that we have 1200 perceptrons in the input layer, then 250, 100 and 3 in the output layer (for 3 labels).
• Construct the layers array to hold
  • ReLU layer with 250 perceptrons
  • ReLU layer with 100 perceptrons
  • Sigmoid (Logistic) layer with as many perceptrons as there are labels (num_labels)
• In neon, don’t forget to add a drop-out layer between each layer
• Run the program on a dataset

analytics $ python mlp/start_training.py \
  -m chest -d ../training-data/labelled/chest -o ./output

• Observe the output; see if you can optimise the hyperparameters

NOTE: You don't need to setup again if you have already ran your environment for the challenge on the previous section

cd analytics
./init-env.sh

To start development on Python code or notebooks

source venv/bin/activate
# After you call this line, you should see (venv) added at the beginning of your shell prompt. 

• In start_analysis.py complete the main method.
• SparkContext is already configured and available as sc.
• Read data from Cassandra database (keyspace training, table data - see conf) The table has the following format user_id | model_id | file_name | time | x | y | z | exercise
• Parallelize the computation for each user_id and model_id combination using training train_model_for_user function using Spark
• Change your directory to mlp and run run_spark.sh to submit your Spark job to Spark cluster