Hsankesara

Hsankesara

Hsankesara

Hsankesara

#include <stdio.h>
#include <string.h>
#include <stdbool.h>
typedef struct Translation Translation;
typedef struct State State;

typedef struct Translation
{
    struct State *nextState;
    char input;

Hsankesara

"""
This is the code for QR factorization using Householder Transformation.
This program is made in python 3.5.3 but will be compatible to any python 3.4+ version
We used numpy library for matrix manipulation.
Install numpy using ** pip3 install numpy ** command on terminal.
To run the code write ** python3 qr_householder.py ** on terminal
User has to give dimension of the matrix as input in space separated format and matrix will be generated randomly.
QR factorization can be done for both square and non-square matrices and hence the code supports both.
"""
import numpy as np

Hsankesara

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Hsankesara

import math
def euclidean_dist(p1, p2):
    return math.sqrt(math.pow(p1[0] - p2[0], 2) + math.pow(p1[1] - p2[1], 2))

def allocate_class(x, means):
    classes = {}
    for clas in means:
        classes[clas] = []
    for idx, point in enumerate(x):
        min_dist = None

Hsankesara

import torch
from torch import nn
import torch.nn.functional as F
import torch.optim as optim

class UNet(nn.Module):
    def contracting_block(self, in_channels, out_channels, kernel_size=3):
        block = torch.nn.Sequential(
                    torch.nn.Conv2d(kernel_size=kernel_size, in_channels=in_channels, out_channels=out_channels),
                    torch.nn.ReLU(),

Hsankesara

#include <stdio.h>
using namespace std;

double get_sum(int m, int n, double **array)
{
    double sum;
    for (int i = 0; i < n; i++)
    {
        for (int j = 0; j < m; j++)
        {