PhanDuc

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class CaptionNet(nn.Module):
    def __init__(self, n_tokens=n_tokens, emb_size=128, lstm_units=256, cnn_feature_size=2048):
        """ A recurrent 'head' network for image captioning. See scheme above. """
        super(self.__class__, self).__init__()
        
        # a layer that converts conv features to 
        self.cnn_to_h0 = nn.Linear(cnn_feature_size, lstm_units)
        self.cnn_to_c0 = nn.Linear(cnn_feature_size, lstm_units)
        
        # recurrent part, please create the layers as per scheme above.

PhanDuc

from sklearn.ensemble import BaggingClassifier

def bootstrap_predictions(estimator, X, y, X_test, n_bootstrap=101):
    """Bootstrap a given classifier.
    
    Parameters
    ----------
    estimator : object
        A classifier instance with sklearn-compatible interface.

PhanDuc

def nearest_neighbours(X, Y, norm=2.0):
    """Index the rows of `Y` in increasing order of distance from each row in `X`.

    Parameters
    ----------
    X : array, 2 dimensional, shape = (n_samples, n_dim)
        The matrix, for the rows of which to find the closest row in `Y`.

    Y : array, 2 dimensional, shape = (n_reference, n_dim)
        The matrix, among the rows of which to seek the closest one for `X`.

PhanDuc

import numpy as np
import scipy as sp
from numpy import random
from numpy import matlib
import matplotlib.pyplot as plt
from gurobipy import GRB
import gurobipy as grb


class Problem:

PhanDuc

import pandas as pd

df = pd.read_excel('D:\\BILL THÁNG 1.xlsx')
du = df.ix[:, 4]
temp_list = []
for cell in du:
    try:
        ed = ''.join(cell.split(',')[-1])
        print(ed)
        temp_list.append(ed)

PhanDuc

'use strict';

const apiai = require('apiai');
const config = require('./config');
const express = require('express');
const crypto = require('crypto');
const bodyParser = require('body-parser');
const request = require('request');
const app = express();
const uuid = require('uuid');