hageldave

from bokeh.server.server import Server
from bokeh.models import Div, ColumnDataSource
from bokeh.plotting import figure
import pandas as pd
import sklearn.datasets as toy_data


def create_application(doc):
    # website titles
    doc.title = "Bokeh Skeleton"

hageldave

# Autoencoder example for dimensionalilty reduction of MNIST
# Requires tensorflow and seaborn
# e.g.: pip install tensorflow-cpu seaborn

import tensorflow as tf
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

hageldave

hageldave

hageldave

/* Copyright 2021 David Haegele - Source from JPlotter */

import java.awt.Color;
import java.awt.Paint;
import java.awt.PaintContext;
import java.awt.Rectangle;
import java.awt.RenderingHints;
import java.awt.Shape;
import java.awt.geom.AffineTransform;
import java.awt.geom.Point2D;

hageldave

hageldave

hageldave

import static org.apache.batik.anim.dom.SVGDOMImplementation.SVG_NAMESPACE_URI;

import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.OutputStreamWriter;
import java.util.Scanner;

import org.apache.batik.anim.dom.SVGDOMImplementation;
import org.apache.batik.transcoder.TranscoderException;

hageldave

import java.awt.Dimension;
import java.awt.Graphics;
import java.awt.event.ComponentAdapter;

import javax.swing.JFrame;
import javax.swing.SwingUtilities;

import org.lwjgl.opengl.GL;
import org.lwjgl.opengl.GL11;
import org.lwjgl.opengl.awt.AWTGLCanvas;

hageldave

clear all;
clc;
more off;

function result = normalize(x)
  l = norm(x);
  if l > 0.00001
    result = x/l;
  else
    result = x;