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Elad Rapaport erap129

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@erap129
erap129 / main.go
Created September 27, 2022 04:26
Snake Part 2 - main.go
package main
import (
"log"
"os"
"github.com/gdamore/tcell"
)
func main() {
@erap129
erap129 / game.go
Created September 27, 2022 04:25
Snake Part 2 - game.go
package main
import (
"time"
"github.com/gdamore/tcell"
)
type Game struct {
Screen tcell.Screen
@erap129
erap129 / snake.go
Created September 27, 2022 04:23
Snake Part 2 - snake.go
package main
type SnakePart struct {
X int
Y int
}
type SnakeBody struct {
Parts []SnakePart
Xspeed int
@erap129
erap129 / snake.go
Created September 26, 2022 07:58
Snake - snake.go
package main
type SnakeBody struct {
X int
Y int
Xspeed int
Yspeed int
}
func (sb *SnakeBody) ChangeDir(vertical int, horizontal int) {
@erap129
erap129 / game.go
Created September 26, 2022 07:54
Snake - game.go
package main
import (
"time"
"github.com/gdamore/tcell"
)
type Game struct {
Screen tcell.Screen
@erap129
erap129 / main.go
Created September 26, 2022 07:29
Snake - main.go
package main
import (
"log"
"os"
"github.com/gdamore/tcell"
)
func main() {
@erap129
erap129 / user_1_ratings.csv
Created September 10, 2022 14:30
User 1 ratings - MovieLens-1M
We can make this file beautiful and searchable if this error is corrected: Unclosed quoted field in line 8.
bucketized_user_age,movie_genres,movie_id,movie_length,movie_title,movie_title_text,timestamp,user_gender,user_id,user_occupation_label,user_occupation_text,user_rating,user_zip_code
1.0,[7],b'150',140,b'Apollo 13 (1995)',b'Apollo 13 (1995)',978301777,False,b'1',17,b'K-12 student',5.0,b'48067'
1.0,[7],b'1193',133,"b""One Flew Over the Cuckoo's Nest (1975)""","b""One Flew Over the Cuckoo's Nest (1975)""",978300760,False,b'1',17,b'K-12 student',5.0,b'48067'
1.0,[ 2 3 12],b'1022',74,b'Cinderella (1950)',b'Cinderella (1950)',978300055,False,b'1',17,b'K-12 student',5.0,b'48067'
1.0,[ 2 3 12],b'595',84,b'Beauty and the Beast (1991)',b'Beauty and the Beast (1991)',978824268,False,b'1',17,b'K-12 student',5.0,b'48067'
1.0,[ 2 3 12 14],b'48',81,b'Pocahontas (1995)',b'Pocahontas (1995)',978824351,False,b'1',17,b'K-12 student',5.0,b'48067'
1.0,[ 2 3 12],b'1029',64,b'Dumbo (1941)',b'Dumbo (1941)',978302205,False,b'1',17,b'K-12 student',5.0,b'48067'
1.0,[12],b'1035',172,"b'Sound of Music, The (1965)'","b'Sound of Music
@erap129
erap129 / tfrs_movielens.py
Last active March 29, 2024 05:37
import argparse
import os
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_recommenders as tfrs
import pickle
import plotly.graph_objects as go
import plotly.io as pio
import re
@erap129
erap129 / user_knn.py
Last active June 7, 2022 17:53
recsys_user_knn_occupation
def get_user_sim_values(train_set):
users_age_gender = (users_df.
assign(inner_id=lambda df: df['user_id'].apply(lambda x: train_set.to_inner_uid(x)),
gender=lambda df: df['gender'].replace({'M': 0, 'F': 1}).astype('int')).
sort_values('inner_id').
filter(items=['age', 'gender'])
)
user_values_np = StandardScaler().fit_transform(users_age_gender.to_numpy())
cosine_sim_user_values = cosine_similarity(user_values_np, user_values_np)
return cosine_sim_user_values
@erap129
erap129 / recsys_genres.py
Created May 6, 2022 07:50
import ast
from itertools import combinations
movies_df['genre_list'] = movies_df['genre'].apply(ast.literal_eval)
tf_genres = TfidfVectorizer(analyzer=lambda s: (c for i in range(1,4) for c in combinations(s, r=i)))
tfidf_genres = tf_genres.fit_transform(movies_df['genre_list'])
item_matrix_genres_trainset_loocv = get_item_matrix_with_inner_ids(tfidf_genres.todense(), movies_df, train_loocv)
cosine_sim_genres_trainset_loocv = cosine_similarity(item_matrix_genres_trainset_loocv,
item_matrix_genres_trainset_loocv)