TomLin

# Definition for singly-linked list.
# class ListNode:
#     def __init__(self, val=0, next=None):
#         self.val = val
#         self.next = next

class Solution:
    def sortList(self, head: Optional[ListNode]) -> Optional[ListNode]:

        if not head or not head.next:

TomLin

# Definition for singly-linked list.
# class ListNode:
#     def __init__(self, val=0, next=None):
#         self.val = val
#         self.next = next

"""
- 在操作linked list上頭，一定要把head保留住，每一次要提取資料，都只能從head，從新開始往下一個個節點找值。
- 這是linked list的特色，在寫leetcode的時候，一定要注意。
- 另一個常用的，就是用current node，來指涉現在要進行處理的node。

TomLin

from typing import List

class Solution:
    def mergeSort(self, nums: List[int]) -> List[int]:

        return self.divide(nums)

    def divide(self, nums):

        if len(nums) <= 1:

TomLin

# 嘗試用recursive的方式，來寫bubble sort

class BubbleSort:

    def __init__(self, array):
        self.arr = array
        self.length = len(self.arr)

    def recur_sort(self, left_round=None):

TomLin

class BubbleSort:

    def __init__(self, array):

        self.arr = array
        self.length = len(self.arr)

    def bubble_sort(self):
        for i in range(self.length):
            left_round = self.length - i

TomLin

class Node:

    def __init__(self, val):

        self.val = val
        self.next = None # 這個next是一個pointer，用來指向後來放的Node


class MyLinked:

TomLin

class MyArray:

    def __init__(self, size):
        self.arr = [None] * size  # 學習如其它正規語言，需要設定list的長度
        self.i_end = -1  # idx, flag for indicating 值已經塞滿了list的空間

    def expand_space(self):
        # 當array的長度不夠時，直接創造一個兩倍長度的array，並且將值複製過去
        arr_new = [None] * (len(self.arr) * 2)
        for i in range(len(self.arr)):

TomLin

def trx_transform(df):
  # REF: https://github.com/moorissa/medium/blob/master/items-recommender/notebooks/recommendation-MT.ipynb
  s = time.time()
  data = pd.melt(frame=df.set_index("customerId")["products"].apply(
      pd.Series).reset_index(),
                id_vars=["customerId"],
                value_name="products") \
  .dropna().drop(["variable"], axis=1) \
  .groupby(["customerId", "products"]) \
  .agg({"products": "count"}) \

TomLin

def get_ncf_model():
    # REF: https://github.com/khuangaf/tibame_recommender_system/blob/master/NCF.ipynb
    user_inp = Input((1,))
    user_hidden = Embedding(input_dim=n_users, output_dim=64)(user_inp)
    user_hidden = Flatten()(user_hidden)
    
    item_inp = Input((1,))
    item_hidden = Embedding(input_dim=n_items, output_dim=64)(item_inp)
    item_hidden = Flatten()(item_hidden)
    

TomLin

def train_test_split(
	ratings,
	split_count,
	fraction=None,
	dir_path=None,
	output_file_long=None,
	user_col: str = None,
	item_col: str = None,
	item_name: str = None
	):