jiahao

# A port of https://github.com/karpathy/llama2.c/blob/master/run.c
# to Julia.
# Jiahao Chen <[email protected]> 2023-07-29
#
# MIT License: see full text at https://opensource.org/license/mit/
#


using LinearAlgebra
using LogExpFunctions

jiahao

using Plots
using StatsPlots
using LinearAlgebra
using ClassicalOrthogonalPolynomials
using ProgressMeter
using Statistics

k = 15 # Size of training data
l = 15 # Size of test data

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using Statistics
using BFloat16s
using StaticArrays
  
import Base: getindex, setindex!, length, iterate

###########################################
# Implementation of the NormedFloat4 type
# and its container type, QLoRAArray
#

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from bs4 import BeautifulSoup
import urllib.request

url = "https://arxiv.org/a/chen_j_2.html"

with urllib.request.urlopen(url) as response:
   html = response.read()

soup = BeautifulSoup(html, 'html.parser')
for link in soup.find_all('a'):

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using Dates: now
using DataFrames

import Base: *, push!
mutable struct AAUpdate{Tu,Tv} # Matrix-free representation of the H matrix
    m::Int #:: Size of the AA subspace
    u::Vector{Tu} #:: The quantities s-Hỹ (note typo in paper)
    v::Vector{Tv} #:: The quantities (H'ŝ)/(ŝ'Hŷ)
end

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# Iterate over SparseMatrixCSC stored entries, ignoring stored zeros and
# missing values.
#
# Implements Julia's new iterator protocol (new as of v0.7)
# Ref: https://julialang.org/blog/2018/07/iterators-in-julia-0.7
#
# Jiahao Chen 2019-12-03
#
# MIT License available upon request
#

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using LinearAlgebra
using StatsBase
using StatsFuns
using NaNMath

"x->2x-1 in place"
function twoxm1!(dat; val=0.0)
    @inbounds for (i,x) in enumerate(dat)
        dat[i] = ifelse(isnan(x), val, 2x-1)
    end

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### Keybase proof

I hereby claim:

  * I am jiahao on github.
  * I am jiahao (https://keybase.io/jiahao) on keybase.
  * I have a public key ASD8uXwFCTxC_HNYH0M6m_5niip3vql6gQ9nqYuUWnkiiQo

To claim this, I am signing this object:

jiahao

struct MultinomialNaiveBayes{T, V<:AbstractVector}
    feature_ratios::V
    prior_ratio::T
end

"""
    fit(MultinomialNaiveBayes, [T,] features, labels, α = 1) -> MNB

fits a `MultinomialNaiveBayes` classifier `MNB` using the
`features` matrix and `labels` vector of `Bool`s.

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struct NaiveBayes{T, V<:AbstractVector, M<:AbstractMatrix}
    probabilities::M
    priors::V
end

train(::Type{NaiveBayes}, T::Type{R}, features, labels, α = 1) where R<:Real =
    train(NaiveBayes{T, Vector{T}, Matrix{T}}, features, labels, α)

for (typ, op) in ((Rational, ://), (Real, :/)) @eval begin
function train(::Type{NaiveBayes{T, S, R}},