const readline = require('readline')
const csv = require('csvtojson')
const ml = require('shaman')
const rl = readline.createInterface({
	input: process.stdin,
	output: process.stdout
})
const csvFilePath = './economicdata1970-2016-ARG.csv'

// Let's get it (SKEREEE)
csv()
	.fromFile(csvFilePath)
	.then(obj => dressData(obj))
	.then(([X, Y]) => performRegression(X, Y))

function dressData(csvData) {
	const header = Object.keys(csvData[0]).filter(
  // remove String fields, this is regression!
		i =>
			i !== 'Year' &&
			i !== 'ISO_Code' &&
			i !== 'Countries' &&
			i !== 'SUMMARY INDEX'
	)

	let X = [],
		Y = []

	csvData.forEach(row => {
		let k = []
		// header.forEach(h => {
		// 	k.push(f(row[h]))
		// })
		k.push(f(row['Government enterprises and investment']))
		k.push(f(row['Government Consumption']))
		X.push(k)
		Y.push(f(row['SUMMARY INDEX']))
	})

	return [X, Y]
}
function performRegression(X, Y) {
	console.log(Y)

	const lr = new ml.LinearRegression(X, Y, {
		algorithm: 'GradientDescent',
		debug: true
	})
	lr.train(err => {
		if (err) console.log(err)
		else predictOutput(lr)
	})
}

function f(s) {
	// return parseFloat(s)
	if (!s) return 0
	return parseFloat(s)
}

function predictOutput(lr) {
	rl.question(
		'Enter input "X,X,X" for prediction (Press Ctrl + C to exit)',
		answer => {
			console.log(f(answer))

			// const response = answer.split(',').map(i => Number(i))
      // HARDCODED
			const response = [
				4,
				5.34,
				21.86,
				6.05,
				15,
				6,
				f(answer),
				7,
				35,
				1,
				58,
				4,
				5.35,
				3.69,
				2.93,
				4.26,
				7.5,
				3.33,
				3.63,
				6.86,
				3.39,
				4.13,
				0.77,
				3.9,
				5.23,
				23.83,
				5.22,
				11.94,
				2,
				40,
				10,
				5.61,
				6.06,
				5.91,
				7.26,
				13.7,
				5.94,
				10.14,
				6.42,
				4.47,
				5.16,
				4.81,
				0,
				5.36,
				0.77,
				7.97,
				4.7,
				3.98,
				5,
				4.26,
				7,
				5.42,
				2.77,
				2.19,
				3.43,
				10,
				2.52,
				10,
				5.15,
				2.4,
				6.67,
				9.12,
				3.26,
				5.25,
				6.51,
				5.54,
				5.37
			]
			console.log('lenght of answer', response.length)

			const prediction = lr.predict([5.34, f(answer)])
			console.log(`At X = ${answer}, y = ${prediction}`)
			predictOutput(lr)
		}
	)
}