Dawny33

# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
# Similarly, for h * W_2 + b_2
import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

Dawny33

import paramiko

k = paramiko.RSAKey.from_private_key_file("YOUR_PEM_FILE.pem")
c = paramiko.SSHClient()
c.set_missing_host_key_policy(paramiko.AutoAddPolicy())
c.connect( hostname = "ec2-1-1-1-1.us-west-2.compute.amazonaws.com", username = "ec2-user", pkey = k )

stdin , stdout, stderr = c.exec_command("hostname")
print("stdout: " + stdout.read())
print("stderr" + stderr.read())

Dawny33

#-*- coding:utf-8 - *-


def load_dataset():
    "Load the sample dataset."
    return [[1, 3, 4], [2, 3, 5], [1, 2, 3, 5], [2, 5]]


def createC1(dataset):
    "Create a list of candidate item sets of size one."