ResearchKit Bookmarks

Open-source internal and extended research, which we use in our internal and commercial projects.

Focus on

• Data Structures and Algorithms
• Machine Learning, Deep Learning, and AI
• Hosting Service
• SaaS/PaaS
• Tools/Frameworks

Algorithms

Butterworth Band Pass

Signal processing filter

• https://en.wikipedia.org/wiki/Butterworth_filter

Python

• https://scipy-cookbook.readthedocs.io/items/ButterworthBandpass.html

iOS

 float A = sqrt(pow(10.0f, (G/20.0f)));
 float beta = sqrt(A / Q);
 
 a0 = (A + 1) - ((A - 1) * omegaC) + (beta * omegaS);
 b0 = (A * ((A + 1) + ((A - 1) * omegaC) + (beta * omegaS)))     / a0;
 b1 = (-2 * A * ((A - 1 ) + ((A + 1) * omegaC)))                 / a0;
 b2 = (A * ((A + 1) + ((A - 1) * omegaC) - (beta * omegaS)))     / a0;
 a1 = (2 * ((A - 1) - ((A + 1) * omegaC)))                       / a0;
 a2 = ((A + 1) - ((A - 1) * omegaC) - (beta * omegaS))           / a0;

k-NN

Pattern recognition https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

An easy way to evaluate the accuracy of the model is to calculate a ratio of the total correct predictions out of all predictions made.

Let’s create a getAccuracy function which sums the total correct predictions and returns the accuracy as a percentage of correct classifications.

Python

def getAccuracy(testSet, predictions):
    correct = 0
    for x in range(len(testSet)):
        if testSet[x][-1] is predictions[x]:
            correct += 1
    return (correct/float(len(testSet))) * 100.0

testSet = [[1,1,1,'a'], [2,2,2,'a'], [3,3,3,'b']]
predictions = ['a', 'a', 'a']
accuracy = getAccuracy(testSet, predictions)
print(accuracy)

Machine Learning and Deep Learning

• Tensorflow - https://github.com/tensorflow
• PyTorch - https://github.com/pytorch/pytorch
• Keras - https://keras.io/api/
• MXNet https://github.com/apache/incubator-mxnet

Neural Network:

• Chainer - https://github.com/chainer/chainer
• Sonne - https://github.com/deepmind/sonnet

Hosting

• Heroku: www.heroku.com
• PythonAnywhere: www.pythonanywhere.com
• AWS Elastic Beanstalk: aws.amazon.com/elasticbeanstalk
• DigitalOcean: www.digitalocean.com
• Microsoft Azure: azure.microsoft.com
• Google Cloud Platform (GCP): cloud.google.com
• Render: render.com
• Vercel: vercel.com

Developement Frameworks

• Ionic: https://ionicframework.com
• Ant Design: https://ant.design/