chelseatroy

class LinearRegressor(Object):
  
    def gradient_descent(self, x, y, alpha, max_iterations):
        num_rows = x.shape[1] + 1
        w = np.array([random.random() for item in range(num_rows)]).reshape(num_rows,1)
        
        best_weights = []
        lowest_cost = None

        iter = 1

chelseatroy

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import autograd.numpy as np  # Thinly-wrapped numpy
from autograd import grad as compute_grad   # The only autograd function you may ever need

class learner():
    def __init__(self,**args):

chelseatroy

chelseatroy

chelseatroy

import math
from scipy.stats import t
import numpy as np

def confidence_interval_for(samples=[], confidence=0.95):
  sample_size = len(samples)
  degrees_freedom = sample_size - 1
  outlier_tails = (1.0 - confidence) / 2.0
  t_distribution_number = -1 * t.ppf(outlier_tails, degrees_freedom)

chelseatroy

import math
from scipy.stats import t
import numpy as np

def confidence_interval_for_collection(sample_size=[], standard_deviation=[], mean=[], confidence=0.95):
  degrees_freedom = [count - 1 for count in sample_size]
  outlier_tails = (1.0 - confidence) / 2.0
  confidence_collection = [outlier_tails for _ in sample_size]
  t_distribution_number = [-1 * t.ppf(tails, df) for tails, df in zip(confidence_collection, degrees_freedom)]

chelseatroy

def t_test_for(num_samples_1, standard_deviation_1, mean_1, num_samples_2, standard_deviation_2, mean_2, confidence=0.95):
  alpha = 1 - confidence
  total_degrees_freedom = num_samples_1 + num_samples_2 - 2

  t_distribution_number = -1 * t.ppf(alpha, total_degrees_freedom)

  degrees_freedom_1 = num_samples_1 - 1
  degrees_freedom_2 = num_samples_2 - 1
  sum_of_squares_1 = (standard_deviation_1 ** 2) * degrees_freedom_1
  sum_of_squares_2 = (standard_deviation_2 ** 2) * degrees_freedom_2

chelseatroy

class Topic < ActiveRecord::Base
...
  def applicable_formulas
    [self, chapter, section, unit, *ancestors].compact.flat_map(&:formula_ids)/code>
  end
end

chelseatroy

require 'rails_helper'

RSpec.describe ReceiptDataSourceTransition do
  describe "get_receipts" do
    before do
      Receipt.delete_all
    end

    it "gets similarly structured responses from both repository and service" do
      #Subjects Under Test

chelseatroy

require 'rails_helper'

RSpec.describe Repositories::ReceiptDataSource do
  describe "get_receipts" do
    before do
      Receipt.delete_all
    end

    it "gets all receipts" do
      @subject_under_test = Repositories::ReceiptDataSource.new