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# 1. Download and install the latest Anaconda distribution from  https://www.continuum.io/downloads#macos

# 2. Create a new Python environment. Make sure you choose python 3.5 as your python version for the virtual environment:

conda create -n myenv python=3.5

# 3. Activate the new environment using:

source activate myenv

vishwanath79

#!/usr/bin/env python
from datetime import datetime
from time import time
from lxml import html, etree
import requests, re
import os, sys
import unicodecsv as csv
import argparse

vishwanath79

Installing Cassandra on Mac OS X

Install Homebrew

Homebrew is a great little package manager for OS X. If you haven't already, installing it is pretty easy:

ruby -e "$(curl -fsSL https://raw.github.com/Homebrew/homebrew/go/install)"

vishwanath79

#%%
import numpy as np
import pandas as pd
data = pd.read_csv("preprocessed_data.csv")
data = data.sample(frac=1)

train_size = int(0.8 * len(data))
features = data.drop(columns=["Price"])
targets= data["Price"]
X_train, X_test = features.values[:train_size, :], features.values[train_size:,:]

vishwanath79

# bully algorithm sample /no sockets
import random


def resurrect(x):
    if running[x] == 1:
        print("Leader is running")
        return
    print("node ", x, " back to life")
    running[x] = 1

vishwanath79

#!/usr/bin/python
# -*- coding: utf-8 -*-
save_best_callback = tf.keras.callbacks.ModelCheckpoint(
    'content/model-{epoch:02d}-{val_acc:.2f}.f5',
    monitor='val_accuracy',
    verbose=1,
    save_best_only=True,
    save_weights_only=False,
    save_freq=100,
    )

vishwanath79

#!/usr/bin/python
# -*- coding: utf-8 -*-
from keras.callbacks import EarlyStopping
save_early_callback = EarlyStopping(monitor='val_loss', min_delta=0,
                                    patience=3, verbose=1,
                                    restore_best_weights=True)
model.fit(
    X_train,
    y_train,
    batch_size=64,

vishwanath79

#!/usr/bin/python
# -*- coding: utf-8 -*-
from keras.callbacks import EarlyStopping, ModelCheckpoint

save_early_callback = EarlyStopping(monitor='val_loss', patience=5)
save_best_callback = \
    ModelCheckpoint('/content/model-{epoch:02d}-{val_accuracy:.2f}.f5'
                    , save_best_only=True, save_weights_only=True)
model.fit(

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