Tooluloope

import csv


ZIP_CODE_FILE_NAME = 'zips.csv'
COVERAGE_PLAN_FILE_NAME = 'plans.csv'
SLCSP_FILE_NAME = 'slcsp.csv'
SLCSP_OUTPUT_FILE_NAME = 'slcsp_answer.csv'

PLAN_TYPE = 'Silver'

Tooluloope

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import time
import os
import argparse

## Load the model 

Tooluloope

def compute_cost(Al, Y):
    
    """
    Arguments:
    AL -- probability vector corresponding to your label predictions, shape (1, number of examples)
    Y -- true "label" vector (for example: containing 0 if non-cat, 1 if cat), shape (1, number of examples)

    Returns:
    cost -- cross-entropy cost
    """

Tooluloope

def linear_activation_forward(W,A_prev,b, activation):
    
    
    """
    Implement the linear part of a layer's forward propagation.

    Arguments:
    A_prev -- activations from previous layer (or input data): (size of previous layer, number of examples)
    W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)
    b -- bias vector, numpy array of shape (size of the current layer, 1)

Tooluloope

def linear_forward(W,A,b):
    """
    Implement the linear part of a layer's forward propagation.
    Arguments:
    A -- activations from previous layer (or input data): (size of previous layer, number of examples)
    W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)
    b -- bias vector, numpy array of shape (size of the current layer, 1)
    Returns:
    Z -- the input of the activation function, also called pre-activation parameter 
    cache -- a python dictionary containing "A", "W" and "b" ; stored for computing the backward pass efficiently

Tooluloope

def initialize_parameters(layer_dims):
    
    """"
    INPUTS:
    layers_dim: represents the dimension of each hidden layer in the neural network, the index(i) represents the 
    layer while layers_dim[i] represents the number of nodes in that layer
    
    RETURN:
    this should return intialized paramenters from W1, b1 to Wl-1, bl-1 assuming l = length of layers_dim
    where Wi is of shape (L[i], L[i-1])

Tooluloope

import numpy as np
import matplotlib.pyplot as plt
import h5py

def load_data():
    train_dataset = h5py.File('./train_catvnoncat.h5', "r")
    train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
    train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels

    test_dataset = h5py.File('./test_catvnoncat.h5', "r")

Tooluloope

import numpy as np
import math   # This will import math module



class KNearestNeighbor(object):
  """ a kNN classifier with L2 distance """

  def __init__(self):
    pass