hackintoshrao

from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
import numpy as np

def pad(sequences, max_len):
    return pad_sequences(sequences, maxlen=max_len)

def categorize_labels(labels) :
    return to_categorical(np.asarray(labels))

hackintoshrao

from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences


def tokenize(top_n_words, texts):
    tokenizer = Tokenizer(num_words=top_n_words)
    tokenizer.fit_on_texts(texts)
    sequences = tokenizer.texts_to_sequences(texts)

    word_index = tokenizer.word_index

hackintoshrao

def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

hackintoshrao

// Write a function 'filter()' that implements a multi-
// dimensional Kalman Filter for the example given
//============================================================================
#include <iostream>
#include "Dense"
#include <vector>

using namespace std;
using namespace Eigen;

hackintoshrao

# Write a program that will iteratively update and
# predict based on the location measurements 
# and inferred motions shown below. 

def update(mean1, var1, mean2, var2):
    new_mean = float(var2 * mean1 + var1 * mean2) / (var1 + var2)
    new_var = 1./(1./var1 + 1./var2)
    return [new_mean, new_var]

def predict(mean1, var1, mean2, var2):

hackintoshrao

def get_batches(arr, n_seqs, n_steps):
    '''Create a generator that returns batches of size
       n_seqs x n_steps from arr.
       
       Arguments
       ---------
       arr: Array you want to make batches from
       n_seqs: Batch size, the number of sequences per batch
       n_steps: Number of sequence steps per batch
    '''

hackintoshrao

import numpy as np

def sigmoid(x):
    """
    Calculate sigmoid
    """
    return 1/(1+np.exp(-x))

def sigmoid_prime(x):
    """

hackintoshrao

# Use histogram on both side of the lane to obtain starting 
    # point for lane line scanning.
    midpoint = np.int(histogram.shape[0]/2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint

    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255

    # Choose the number of sliding windows
    nwindows = 9

hackintoshrao

import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

image = mpimg.imread('cutout1.jpg')

# Define a function to compute color histogram features  
def color_hist(img, nbins=32, bins_range=(0, 256)):
    # Compute the histogram of the RGB channels separately

hackintoshrao

import np as numpy
import cv2

"""
draw rectangle boxes at given coordinates on the given image.
"""
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
    # make a copy of the image
    draw_img = np.copy(img)
    # draw each bounding box on your image copy using cv2.rectangle()