skojaku

# Change the prefix
set -g prefix C-s

# Set vim-style key bindings
setw -g mode-keys vi

# Start selection with 'v' in copy mode
# Added because 'v' selection wasn't working despite 'V' line selection working
bind -T copy-mode-vi v send -X begin-selection

skojaku

#!/bin/bash
# doi_or_title_fetcher.sh
# Usage examples:
#   ./doi_or_title_fetcher.sh "10.1145/3511808.3557220"
#   ./doi_or_title_fetcher.sh "Promptagator Few-shot dense retrieval from 8 examples"
#
# Install bibtex-tidy to use this script: 
# >> npm install -g bibtex-tidy
#

skojaku

"""
A script to generate Marp presentation slides from quiz questions stored in JSON format.

This script takes a JSON file containing quiz questions and generates a Markdown file formatted
for Marp presentations. Each question is placed on its own slide. The output follows Marp's
markdown syntax with appropriate slide separators and formatting.

Functions:
    create_marp_header() -> str: Creates the YAML front matter for Marp slides
    format_question_slide(question_data: dict) -> str: Formats a single question into a slide

skojaku

import numpy as np
from scipy import sparse
from tqdm import tqdm
import numba
from numba import jit, float64, int64
from numba.experimental import jitclass

def calc_ppr_forward_push_fast(adj, source_nodes=None, alpha=0.15, epsilon=1e-6, batch_size=1000):
    """
    Compute Personalized PageRank (PPR) scores for specified source nodes using a numba-accelerated forward push method.

skojaku

import numpy as np
from scipy import sparse


#
# Randomized SVD
#
def rSVD(X, dim, **params):
    if isinstance(X, list):
        return _rSVD_submatrices(X, r=dim, **params)

skojaku

from tqdm import tqdm
import numpy as np
import pandas as pd
import scipy.sparse as sparse


def preferential_attachment_model_empirical(
    t0, nrefs, net_train, t_start, mu=None, sig=None, c0=20, n0=0
):
    """

skojaku

# Efraimidis and Spirakis algorithm
# https://www.sciencedirect.com/science/article/abs/pii/S002001900500298
import numpy as np
from numba import njit


@njit(nogil=True)
def one_pass_sampling_without_replacement(n, k, weights):
    # Draw a uniform random variable for each item
    u = np.random.rand(n)

skojaku

import numpy as np 
from scipy import sparse 

#
# Evaluation
#
def calc_esim(y, ypred, normalize=False):
    """
    Element centric similarity.
    

skojaku

"""Non-backtracking spectral embedding

Reference
---------
Krzakala, Florent, et al. "Spectral redemption in clustering sparse networks." Proceedings of the National Academy of Sciences 110.52 (2013): 20935-20940.


Example
-------
```python

skojaku

# -*- coding: utf-8 -*-
# @Author: Sadamori Kojaku
# @Date:   2022-10-05 06:24:53
# @Last Modified by:   Sadamori Kojaku
# @Last Modified time: 2023-05-19 13:04:26
import sys
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
import torch