suzusuzu

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from mpl_toolkits.mplot3d import Axes3D

def kde(data, sigma):
    def f(x):
        l = x.shape[0]
        res = np.zeros(l)
        for i in range(l):

suzusuzu

import numpy as np

def rosenbrock(x):
    end = x.shape[0]
    a = 100.0
    b = 1.0
    return np.sum(a * np.power((x[1:] - np.power(x[:end-1], 2)), 2) + np.power((x[:end-1] - b), 2))

def cmaes(dim, f):
    # User defined parameters

suzusuzu

suzusuzu

import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors

def kl_d_norm(mu1, sigma1, mu2, sigma2):
    d = np.log(sigma2/sigma1)
    d += (sigma1**2 + (mu1 - mu2)**2) / (2 * sigma2**2)
    d -= 1/2
    return d

suzusuzu

using Cascadia
using Gumbo
using HTTP
using JSON
using DataFrames
using Dates
using Plots

# scraping
r = HTTP.request("GET", "https://paperswithcode.com/sota/image-classification-on-imagenet")                      

suzusuzu

suzusuzu

const arr = [...Array(100).keys()];

const arr1 = arr.map(x => x*x % 2 == 0 ? x*x : null).filter((x): x is number => x !== null);
// arr1: number[]

const arr2 = arr.flatMap(x => x*x % 2 == 0 ? [x*x] : [])
// arr2: number[]

suzusuzu

using Random
using LinearAlgebra
using DataStructures
using Base

mutable struct Node
    data
    friend::Set{Node}
end

suzusuzu

Keybase proof

I hereby claim:

• I am suzusuzu on github.
• I am suzusuzu (https://keybase.io/suzusuzu) on keybase.
• I have a public key ASBFQYSStcXvfZPPinQlrdVnRQ3qJCbsIUwtJYQOHPdDywo

To claim this, I am signing this object: