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from csv import reader
from hashlib import sha512
import sys

# Simple merkle tree example
# Invoke the script by calling "python merkle_sample.py "file1.csv" "file2.csv" to compare two merkle trees
class Merkle:

    def __init__(self, file):
        self.file = file

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#!/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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#!/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,

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#!/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,
    )

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# 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

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#%%
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

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)"