dyerrington

Attach to console

After running geth for a while, I found that I could no longer attach to a running session. The message I encountered was Fatal: Unable to attach to remote geth: Timed out waiting for pipe '\\.\pipe\geth.ipc' to come available. I was able to resolve this by attaching to the RPC (?) endpoint.

geth attach http://127.0.0.1:8545

Check balance

Logging into the console (attach to a running session), to get current amount of Ethereum:

dyerrington

import os, sys
from time import sleep

## We could update this to all files in all subdirectories
watching = [__file__]
watched_mtimes = [(f, os.path.getmtime(f)) for f in watching]

while True:
    print("Idle...")
    sleep(2) ## So not to kill our CPU cycles

dyerrington

from flask import Flask, jsonify, request
from sklearn.linear_model import LogisticRegression

## additional imports
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.externals import joblib
from sklearn.datasets import load_iris
import numpy as np

dyerrington

@app.route('/predict-iris')
def predict_iris():

    # Load data
    iris = load_iris()

    # print("Loaded iris", iris)

    # Fit our model
    logreg = LogisticRegression()

dyerrington

@app.route('/boosted')
def boosted():

    input_sepal_len = request.args.get("sepal_len")
    input_sepal_width = request.args.get("sepal_width")
    input_petal_lengh = request.args.get("petal_lengh")
    input_petal_width = request.args.get("petal_width")

    if input_sepal_len:

dyerrington

@app.route('/ian')
def ian():

    from sklearn.datasets import load_iris
    import pandas as pd

    data = load_iris()
    df = pd.DataFrame(data['data'], columns=['sepal_len', 'sepal_width', 'petal_lengh', 'petal_width'])
    y = data['target']

dyerrington

from sklearn.neighbors import NearestNeighbors

nn = NearestNeighbors(n_neighbors=150, radius=10)
model = nn.fit(artist_genre)
A = model.radius_neighbors_graph(artist_genre)
artist_neighbors = pd.DataFrame(A.toarray(), columns=artist_sim.index, index=artist_sim.index)
artist_neighbors['2Pac'].sort_values(ascending=False).head(150)

dyerrington

from sklearn.linear_model import ElasticNetCV
from sklearn.datasets import load_boston

data = load_boston()
df = pd.DataFrame(data['data'], columns=data['feature_names'])

X, y = df, data['target']

elastic = ElasticNetCV(cv=5)
model = elastic.fit(X, y )

dyerrington

import ipywidgets as widgets
from IPython.display import clear_output, display
import re

raw_review = widgets.Textarea(
    value='',
    placeholder='Paste review text here',
    layout=widgets.Layout(width='50%', height='80px'),
)

dyerrington