justanotherminh

class Parser:
    def __init__(self, text):
        self.text = ''.join(text.split())
        self.pos = 0

    def parse(self):
        return self.parse_expression()

    def parse_expression(self):
        result = self.parse_term()

justanotherminh

#include <openssl/evp.h>
#include <openssl/kdf.h>
#include <openssl/objects.h>
#include <openssl/crypto.h>
#include <openssl/core_names.h>
#include <openssl/obj_mac.h>

#include <openssl/bio.h>
#include <openssl/err.h>
#include <openssl/ec.h>

justanotherminh

import numpy as np
import time
np.random.seed(281)


def generate_graph(n, p, k):
    G = (np.random.rand(n, n) <= p).astype(np.int32)
    G[np.triu_indices(n)] = 0
    # Random integer weighing scheme
    for x in np.nditer(G, op_flags=['writeonly']):

justanotherminh

import java.io.*;
import java.util.*;

class Main {
    public static void main(String[] args) throws IOException {
        File file = new File(args[0]);
        char[][] board = new char[9][9];
        BufferedReader buffer = new BufferedReader(new FileReader(file));
        for (int i = 0; i < 9; i++) {
            for (int j = 0; j < 9; j++) {

justanotherminh

#Library Paths
LIB_OPENCV4_PATH=/usr/local/Cellar/opencv/4.1.0_2/lib
LIB_RS2_PATH=/Users/nguyenluongminh/librealsense/build

#Include Paths
INCLUDE_OPENCV4_PATH=/usr/local/Cellar/opencv/4.1.0_2/include/opencv4
INCLUDE_RS2_PATH =/Users/nguyenluongminh/librealsense/include/librealsense2
INCLUDE_IMGUI_PATH =/Users/nguyenluongminh/librealsense/build

#Linker

justanotherminh

from fractions import Fraction
import numpy as np


def print_matrix(matrix):
    def tostr(i):
        return str(i) if i.denominator == 1 else '{}\\{}'.format(i.numerator, i.denominator)
    print '\n'.join(['\t'.join([tostr(col) for col in row]) for row in matrix])

justanotherminh

    {
        "package": {
            "version": "3.1",
            "division": [
                {
                    "alias": "tx2_64bit",
                    "selected": "1",
                    "name": "Jetson TX2"
                },
                { 

justanotherminh

import numpy as np


def sigmoid(v):
    return 1 / (1 + np.exp(-v))


def one_hot(x):
    N = x.size
    D = x.max() + 1

justanotherminh

# Sometimes it works, sometimes it doesn't
import numpy as np
import gym


class Net(object):
    def __init__(self, input, output):
        # W = 0.01 * np.random.randn(input, output)
        # b = np.zeros([1, output])  # Random initialization
        W = np.array([[-0.60944746, 0.45539405],

justanotherminh

import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data


def sample_vector(v, w, b, bernoulli=True):
    h = 1 / (1 + np.exp(-(v.dot(w) + b)))
    if bernoulli:
        h = np.random.binomial(1, h)
    return h