AdroitAnandAI

# Word Count using Spark
# https://spark.apache.org/examples.html

text_file = sc.textFile("./sample.txt") //local file
counts = text_file.flatMap(lambda line: line.split(" ")) \
             .map(lambda word: (word, 1)) \
             .reduceByKey(lambda a, b: a + b)

counts.saveAsTextFile("./count_output")

AdroitAnandAI

# Compute value of PI using Monte Carlo Approach
# Source: http://spark.apache.org/examples.html
NUM_SAMPLES = 1000000

def inside(p):
    x, y = random.random(), random.random()
    return x*x + y*y < 1

# filter: http://spark.apache.org/docs/latest/api/python/pyspark.html
count = sc.parallelize(range(0, NUM_SAMPLES),10) \

AdroitAnandAI

function [J, grad] = cofiCostFunc(params, Y, R, num_users, num_movies, ...
                                  num_features, lambda)

% Unfold the U and W matrices from params
X = reshape(params(1:num_movies*num_features), num_movies, num_features);
Theta = reshape(params(num_movies*num_features+1:end), ...
                num_users, num_features);

% You need to return the following values correctly
J = 0;

AdroitAnandAI

function [mu sigma2] = estimateGaussian(X)
% To estimate parameters of Gaussian distribution using data

% Useful variables
[m, n] = size(X);

% You should return these values correctly
mu = zeros(n, 1);
sigma2 = zeros(n, 1);

AdroitAnandAI

# Training Module of LSTM 2 layer stack

max_review_length = 600
X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)

# To train the Model
def trainModel(model):

    history = model.fit(numpy.array(X_train), numpy.array(y_train), \

AdroitAnandAI

# Speech Recognition Thread identifies the command from defined synonyms and inserting to stt Queue
def stopStream(stream_reader):

    if stream_reader:
        stream_reader.stop_stream()
        stream_reader = None

def received_frames(frames, speech, stt):

    speech.push_data(frames, finish_processing=False)

AdroitAnandAI

import os
import cv2

from queue import Queue 
from threading import Thread, Event

from audio import audio_helper
from speech_library.speech_manager import SpeechManager
from speech_library.speech_proxy import SPEECH_CONFIG

AdroitAnandAI

# To detect wink from stream of eye images and print the sequence of Event trigger
while(runLoop):

    for i in range (1000):

        # read the image
        image = cv2.imread("eyeImages_day/eye"+str(i) + ".jpg", 1)

        # Convert to gray scale as histogram works well on 256 values.
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

AdroitAnandAI

# Code to fit the inverse sigmoid curve to tail end of signal

def sigmoid(x, L ,x0, k, b):

    y = L / (1 + np.exp(k*(x-x0)))+b
    return (y)

def isCurveSigmoid(pixelCounts, count):
    try:
        xIndex = len(pixelCounts)

AdroitAnandAI

    # yaw and pitch are important for mouse control
    poseArrowX = orientation[0] #* arrowLength
    poseArrowY = orientation[1] #* arrowLength

    # Taking 2nd and 3rd row for 2D Projection
    ############################# LEFT EYE ###################################
    cv2.arrowedLine(frame, leftEye_Center,
              (int((xCenter_left + arrowLength * (cosR * cosY + sinY * sinP * sinR))),
              int((yCenter_left + arrowLength * cosP * sinR))), (255, 0, 0), 4)