| from builtins import range | |
| import time | |
| from contextlib import contextmanager | |
| from concurrent.futures import as_completed | |
| import click | |
| from requests import Session | |
| from requests_futures.sessions import FuturesSession # v0.9.7 | |
Download the VM image from osboxes.org and follow the guide to setup the VM.
The username and passwords are:
Open the VM, install guest additions to allow full screen: click the devices menu, and choose the last item, "install guest additions...". You'll probably need to restart the VM afterwords.
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from scipy.integrate import odeint | |
| import seaborn as sns | |
| def dvdt(v, t, alpha, rho, beta): | |
| x, y, z = v | |
| return alpha * (y - x), x * (rho - z) - y, x * y - beta * z | |
| alpha = 10 |
| def gompertz(t, N0, K, r, T): | |
| return K * np.exp(-T * np.exp(-r * t)) | |
| popt, cov = curve_fit(gompertz, t, N, (N.min(), N.max(), 1, 2)) | |
| N0, K, r, T = popt | |
| Nhat = gompertz(t, N0, K, r, T) | |
| print('N0={0:.3f}, K={1:.3f}, r={2:.6f}, T={3:.4f}'.format(N0, K, r, T)) | |
| print("MSE:", ((N - Nhat)**2).mean()) | |
| plt.scatter(t, N) |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import sklearn.datasets | |
| import sklearn.cross_validation as cv | |
| from sklearn import linear_model | |
| dataset = sklearn.datasets.fetch_california_housing() | |
| X = dataset['data'] | |
| y = dataset['target'] |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| import sys | |
| import csv | |
| try: | |
| import click | |
| except ImportError as e: | |
| print("Please install Click: http://click.pocoo.org") | |
| sys.exit(1) |
| # gist at https://gist.github.com/c1f71ca235a7ef217df3 | |
| [user] | |
| email = [email protected] | |
| name = Yoav Ram | |
| [push] | |
| default = simple | |
| [core] | |
| pager = less -r | |
| autocrlf = true | |
| # http://blog.martinfenner.org/2014/08/25/using-microsoft-word-with-git/ |