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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() |
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#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> |
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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']): |
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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++) { |
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#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 |
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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]) | |
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import numpy as np | |
def sigmoid(v): | |
return 1 / (1 + np.exp(-v)) | |
def one_hot(x): | |
N = x.size | |
D = x.max() + 1 |
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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 |
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{ | |
"package": { | |
"version": "3.1", | |
"division": [ | |
{ | |
"alias": "tx2_64bit", | |
"selected": "1", | |
"name": "Jetson TX2" | |
}, | |
{ |
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# 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], |
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