import { factory } from '../../../utils/factory.js'
import { createSolveValidation } from './utils/solveValidation.js'

const name = 'lsolveAll'
const dependencies = [
  'typed',
  'matrix',
  'divideScalar',
  'multiplyScalar',
  'subtract',
  'equalScalar',
  'DenseMatrix'
]

export const createLsolveAll = /* #__PURE__ */ factory(name, dependencies, ({ typed, matrix, divideScalar, multiplyScalar, subtract, equalScalar, DenseMatrix }) => {
  const solveValidation = createSolveValidation({ DenseMatrix })

  /**
   * Finds all solutions of a linear equation system by forwards substitution. Matrix must be a lower triangular matrix.
   *
   * `L * x = b`
   *
   * Syntax:
   *
   *    math.lsolveAll(L, b)
   *
   * Examples:
   *
   *    const a = [[-2, 3], [2, 1]]
   *    const b = [11, 9]
   *    const x = lsolveAll(a, b)  // [ [[-5.5], [20]] ]
   *
   * See also:
   *
   *    lsolve, lup, slu, usolve, lusolve
   *
   * @param {Matrix, Array} L       A N x N matrix or array (L)
   * @param {Matrix, Array} b       A column vector with the b values
   *
   * @return {DenseMatrix[] | Array[]}  An array of affine-independent column vectors (x) that solve the linear system
   */
  return typed(name, {

    'SparseMatrix, Array | Matrix': function (m, b) {
      return _sparseForwardSubstitution(m, b)
    },

    'DenseMatrix, Array | Matrix': function (m, b) {
      return _denseForwardSubstitution(m, b)
    },

    'Array, Array | Matrix': function (a, b) {
      const m = matrix(a)
      const R = _denseForwardSubstitution(m, b)
      return R.map(r => r.valueOf())
    }
  })

  function _denseForwardSubstitution (m, b_) {
    // the algorithm is derived from
    // https://www.overleaf.com/read/csvgqdxggyjv

    // array of right-hand sides
    const B = [solveValidation(m, b_, true)._data.map(e => e[0])]

    const M = m._data
    const rows = m._size[0]
    const columns = m._size[1]

    // loop columns
    for (let i = 0; i < columns; i++) {
      let L = B.length

      // loop right-hand sides
      for (let k = 0; k < L; k++) {
        const b = B[k]

        if (!equalScalar(M[i][i], 0)) {
          // non-singular row

          b[i] = divideScalar(b[i], M[i][i])

          for (let j = i + 1; j < columns; j++) {
            // b[j] -= b[i] * M[j,i]
            b[j] = subtract(b[j], multiplyScalar(b[i], M[j][i]))
          }
        } else if (!equalScalar(b[i], 0)) {
          // singular row, nonzero RHS

          if (k === 0) 
 else {
            // This RHS is invalid but other solutions may still exist
            B.splice(k, 1)
            k -= 1
            L -= 1
          }
        } else if (k === 0) {
          // singular row, RHS is zero

          const bNew = [...b]
          bNew[i] = 1

          for (let j = i + 1; j < columns; j++) {
            bNew[j] = subtract(bNew[j], M[j][i])
          }

          B.push(bNew)
        }
      }
    }

    return B.map(x => new DenseMatrix({ data: x.map(e => [e]), size: [rows, 1] }))
  }

  function _sparseForwardSubstitution (m, b_) {
    // array of right-hand sides
    const B = [solveValidation(m, b_, true)._data.map(e => e[0])]

    const rows = m._size[0]
    const columns = m._size[1]

    const values = m._values
    const index = m._index
    const ptr = m._ptr

    // loop columns
    for (let i = 0; i < columns; i++) {
      let L = B.length

      // loop right-hand sides
      for (let k = 0; k < L; k++) {
        const b = B[k]

        // values & indices (column i)
        const iValues = []
        const iIndices = []

        // first & last indeces in column
        const firstIndex = ptr[i]
        const lastIndex = ptr[i + 1]

        // find the value at [i, i]
        let Mii = 0
        for (let j = firstIndex; j < lastIndex; j++) {
          const J = index[j]
          // check row
          if (J === i) {
            Mii = values[j]
          } else if (J > i) {
            // store lower triangular
            iValues.push(values[j])
            iIndices.push(J)
          }
        }

        if (!equalScalar(Mii, 0)) {
          // non-singular row

          b[i] = divideScalar(b[i], Mii)

          for (let j = 0, lastIndex = iIndices.length; j < lastIndex; j++) {
            const J = iIndices[j]
            b[J] = subtract(b[J], multiplyScalar(b[i], iValues[j]))
          }
        } else if (!equalScalar(b[i], 0)) {
          // singular row, nonzero RHS

          if (k === 0) {
            // There is no valid solution
            return []
          } else {
            // This RHS is invalid but other solutions may still exist
            B.splice(k, 1)
            k -= 1
            L -= 1
          }
        } else if (k === 0) {
          // singular row, RHS is zero

          const bNew = [...b]
          bNew[i] = 1

          for (let j = 0, lastIndex = iIndices.length; j < lastIndex; j++) {
            const J = iIndices[j]
            bNew[J] = subtract(bNew[J], iValues[j])
          }

          B.push(bNew)
        }
      }
    }

    return B.map(x => new DenseMatrix({ data: x.map(e => [e]), size: [rows, 1] }))
  }
})

{
            // There is no valid solution
            return []
          }