andiac

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

output_size = 501
flow = np.zeros((output_size, output_size, 2), dtype=np.float32)

flow[:, :, 0] = np.arange(-(output_size // 2), output_size // 2 + 1)
flow[:, :, 1] = np.arange(-(output_size // 2), output_size // 2 + 1)[:, np.newaxis]

andiac

andiac

import torch

batch_size = 3
seq_len = 20
vocab_size = 4
emb_size = 6
conv_length = 2
num_conv_kernel = 30
channel_in = 1 # of course for text...

andiac

def evaluate_ECE(data_iter, net, device, M=15):
    net.eval()

    bins, n = [0.0 for _ in range(M)], 0

    with torch.no_grad():
        for X_batch, y_batch in data_iter:
            p_batch = torch.exp(lsoftmax(net(X_batch.to(device))))
            y_hat_batch = net(X_batch.to(device)).argmax(dim=1)
            for p, y_hat, y in zip(p_batch.numpy(), y_hat_batch.numpy(), y_batch.numpy()):

andiac

ai_service_enable = "false"
ai_service_mode = "0"
ai_service_pause = "false"
ai_service_source_lang = "0"
ai_service_target_lang = "0"
ai_service_url = "http://localhost:4404/"
all_users_control_menu = "true"
apply_cheats_after_load = "false"
apply_cheats_after_toggle = "false"
aspect_ratio_index = "0"

andiac

/*
 *  Copyright (C) 2020-2021 Andi Zhang <[email protected]>
 */

#include <iostream>

using namespace std;

class MyInt {
public:

andiac

import random
import sys
import time

# sys.setrecursionlimit(10000)

def qsort(a):
    if len(a) <= 1: return a
    pivot = random.choice(a)
    left = qsort([x for x in a if x < pivot])

andiac

andiac

andiac

from collections import deque

class TrieNode:
    def __init__(self, endWord = False, parent = None, char = ''):
        # to record duplicate words
        self.count = int(endWord)
        self.children = dict()
        # fail pointer of Aho Corasick Automaton
        self.fail = None
        # recording the parent in order to output the matched concept