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import numpy as np
import torch

from recbole.data.interaction import Interaction


@torch.no_grad()
def full_sort_scores(uid_series, model, test_data, device=None):
    """Calculate the scores of all items for each user in uid_series.
    Note:

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import Select from 'react-select'
import { List } from 'react-virtualized'
import React, { useState, Component } from 'react'
import { filter } from 'lodash/fp'

const MenuList = props => {
  const rows = props.children
  const rowRenderer = ({ key, index, isScrolling, isVisible, style }) => (
    <div key={key} style={style}>
      {rows[index]}

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def fast_access_sparse_matrix(idx, row, sparse_matrix):
    # index
    start, stop = sparse_matrix.indptr[idx], sparse_matrix.indptr[idx+1]
    index_data = sparse_matrix.data[start:stop]
    index_rows = sparse_matrix.indices[start:stop]
    # row
    ## find row index
    if row in index_rows:
        row_index = np.where(index_rows==row)[0][0]
        extract_elements = index_data[row_index]

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import math
def precision_and_recall(ranked_list, ground_list):
    hits = 0
    for i in range(len(ranked_list)):
        id = ranked_list[i]
        if id in ground_list:
            hits += 1
    pre = hits / (1.0 * len(ranked_list))
    rec = hits / (1.0 * len(ground_list))
    return pre, rec

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---
title: "pokemontest"
output: html_document
---

## 列数
```{r}
#データの読み込み
df<-read.csv('../dataset/pokemon.csv',encoding = 'utf-8',sep='\t')
#列数の取得