{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpolation Layer\n",
    "\n",
    "Create an Interpolation relation in the neural network model. \n",
    "\n",
    "This block performs linear interpolation of an input tensor `x` given two vectors of points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>-- nnodely_v1.5.0 --<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n"
     ]
    }
   ],
   "source": [
    "# uncomment the command below to install the nnodely package\n",
    "#!pip install nnodely\n",
    "\n",
    "from nnodely import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Usage\n",
    "\n",
    "Create an interpolation layer passing two vectors of points. the two vectors must have the same shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = Input('x')\n",
    "interpolation = Interpolation(x_points=[1.0, 2.0, 3.0, 4.0],y_points=[1.0, 4.0, 9.0, 16.0])(x.last())\n",
    "out = Output('out',interpolation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Interpolation\n",
    "\n",
    "Create a linear interpolation layer passing two vectors of points.\n",
    "\n",
    "!! Note that the points do not necessarily need to be ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33m[check_names] The name 'out' is already in defined as NeuObj but it is overwritten.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "y = Input('y')\n",
    "interpolation = Interpolation(x_points=[1.0, 4.0, 3.0, 2.0],y_points=[1.0, 16.0, 9.0, 4.0], mode='linear')(y.last())\n",
    "out = Output('out',interpolation)"
   ]
  }
 ],
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