RikiyaOta

import numpy as np

class LogisticRegressionGradientDescent:

    def __init__(self, eta=0.05, n_iter=100, random_state=1):
        self.eta = eta
        self.n_iter = n_iter
        self.random_state = random_state

    def fit(self, X, y):

RikiyaOta

import numpy as np

class Perceptron:
  
    def __init__(self, eta=0.01, n_iter=50, random_state=1):
      　"""
　      eta: 学習率
        n_iter: 学習を繰り返す回数
        """
        self.eta = eta

RikiyaOta

import numpy as np

class AdalineByGradinentDescent:
  
  def __init__(self, eta=0.01, n_iter=10, random_state=1):
    """
    eta: 学習率
    n_iter: 学習を反復する回数
    """
    self.eta = eta

RikiyaOta

  pipeline :browser do
    plug :accepts, ["html"]
    plug :fetch_session
    plug :fetch_flash
    plug :protect_from_forgery
    plug :put_secure_browser_headers
    plug :set_log1_file_daily
    plug :set_log2_file_daily
  end  
  

RikiyaOta

# Configures backend :log1
config :logger, :log1,
  path: Path.join([File.cwd!, "var", "log", "log1.log"]),
  level: :info,
  metadata_filter: [type: :log1]

# Configures backend :log2
config :logger, :log2,
  path: Path.join([File.cwd!, "var", "log", "log2.log"]),
  level: :debug,

RikiyaOta

# Configures backend :log1
config :logger, :log1,
  path: Path.join([File.cwd!, "var", "log", "log1.log"]),
  level: :info

# Configures backend :log2
config :logger, :log2,
  path: Path.join([File.cwd!, "var", "log", "log2.log"]),
  level: :debug

RikiyaOta

# Configures logger_file_backend
config :logger,
  backends: [:console, {LoggerFileBackend, :log1}, {LoggerFileBackend, :log2}]

RikiyaOta

defp deps do
  [
    {:logger_file_backend, "~> 0.0.10"}
  ]
end

RikiyaOta

const arr = [1,2,3,4,5];

RikiyaOta

function sumOfArray(arr, sum = 0){
  if(arr.lenth === 0) return sum;
  else{
    const [first, ...rest] = arr;
    return sumOfArray(rest, first + sum);
  }
}