AlexTitovWork

# load yolov3 model and perform object detection
# based on https://github.com/experiencor/keras-yolo3
# Partically coded by Interceptor 16.09.2021
# @name Alex Titov
# @email alexeytitovwork@gmail.com
import numpy as np
from numpy import expand_dims
from keras.models import load_model
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array

AlexTitovWork

import numpy as np
# ################################################################################
# Newton's method
# Second order method
def Newtons_method(max_iters, threshold, XY_init, func, grad_func, second_grad, learning_rate=0.05):
    X, Y = XY_init
    w = np.array([X, Y])
    w_history_N = X, Y
    f_history_N = func(X, Y, extra_param)
    delta_w = np.zeros(XY_init.shape)

AlexTitovWork

import numpy as np
# ################################################################################
# Gradient descent or Cauchy method
# First order method
def gradient_descent(max_iters, threshold, XY_init, func, grad_func, learning_rate=0.05):
    X, Y = XY_init
    w = np.array([X, Y])
    w_history = X, Y
    f_history = func(X, Y, extra_param)
    delta_w = np.zeros(XY_init.shape)

AlexTitovWork

import numpy as np
import matplotlib.pyplot as plt
# ################################################################################
def plot_function():
    # Make data.
    X = np.arange(-5, 5, 0.25)
    Y = np.arange(-5, 5, 0.25)
    X, Y = np.meshgrid(X, Y)
    Z = (X ** 2 + Y - 11) ** 2 + (X + Y ** 2 - 7) ** 2

AlexTitovWork

   /**
     * Build prepared classifier on the training data
     */
    public void fit() {
        try {
            classifier.buildClassifier(trainData);
        } catch (Exception e) {
            LOGGER.warning(e.getMessage());
        }
    }

AlexTitovWork

   /**
     * load training data and set feature generators
     */
    public void transform() {
        try {
            trainData = loadDataset(TRAIN_DATA);
            saveArff(trainData, TRAIN_ARFF_ARFF);
            /**
             * create the filter and set the attribute to be transformed from text into a feature vector (the last one)
             */

AlexTitovWork

 private static Logger LOGGER = Logger.getLogger("DebitCreditInternalSystem");

    private FilteredClassifier classifier;
    /**
     * Declare train and test data Instances
     */
    private Instances trainData;
    /**
     * Declare Instance's attributes 
     */

AlexTitovWork

 DebitCreditWekaClassifier() {
        /*
         * Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
         */
        classifier = new FilteredClassifier();
        /**
         * Class for building and using a multinomial Naive Bayes classifier. For more information see,
         * Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification.
         * https://weka.sourceforge.io/doc.dev/weka/classifiers/bayes/NaiveBayesMultinomial.html
         */

AlexTitovWork

/**
 * Naive Bayes
 */
import weka.classifiers.bayes.NaiveBayesMultinomial;
/**
 * Weka tools
 */
import weka.classifiers.Evaluation;
import weka.classifiers.meta.FilteredClassifier;
import weka.core.Attribute;

AlexTitovWork

import java.io.IOException;

public class NlpProductClassifier {

     public static void main(String[] args) throws IOException {

         NLPClassifier CL = new NLPClassifier();
         /**
          * Sentence detector test
          */