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piyush01123

import numpy as np

def integrate(f,start,end,dx=.001):
	x_points = np.arange(start,end,dx)
	y_points = [f(x) for x in x_points]
	area = dx*(sum(y_points[1:-1])+0.5*(y_points[0]+y_points[-1]))
	# area = dx*sum(y_points)
	return area

def main():

piyush01123

#include <iostream>
void printArray(int *A, int n){
	for (int i=0;i<n;i++){ std::cout<<A[i]<<' ';}
	std::cout<<'\n';
}
void merge(int *A, int *left, int *right, int n1, int n2){
	int i=0,j=0,k=0;
	while (i<n1 && j<n2){
		if (left[i]<right[j]){A[k]=left[i];i++;k++;}
		else {A[k]=right[j];j++;k++;}

piyush01123

/*
Huffman Encoding: Given a frequency map of signs, create a new compressed representation.
Algorithm:
1. Create a data structure for node with prob, sign, left, right pointers
2. Create a min heap of nodes with prob as key. For each item in frequency map; insert node(p, s, NULL, NULL) into the min heap.
3. while the min heap has more than 1 element: Do
 a) pop two nodes
 b) create a parent node with these two nodes as its children
 c) push parent node into the min heap
4. The last remaining node in the min heap is the root of the Huffman Tree.

piyush01123

from torchvision import models
import torch
import cv2
import numpy as np

items = ['alexnet', 'densenet121', 'densenet161', 'densenet169', 'densenet201',
         'googlenet', 'mnasnet0_5', 'mnasnet0_75', 'mnasnet1_0', 'mnasnet1_3',
         'mobilenet_v2', 'resnet101', 'resnet152', 'resnet18', 'resnet34', 'resnet50', 
         'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5', 'shufflenet_v2_x2_0', 

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# demonstrates plotting ROC curve and getting AUC score
import numpy as np

def roc_curve(y_true, y_pred):
    fpr = []
    tpr = []
    thresholds = np.arange(0.0, 1.01, .01)

    P = sum(y_true)
    N = len(y_true) - P

piyush01123

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
import scipy.stats

digits = datasets.load_digits()
trainX, testX, trainY, testY = train_test_split(digits.images, digits.target, test_size=.2)

Y_freq = {y: sum(trainY==y) for y in range(10)}