| from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin | |
| class _ResidualEstimator(BaseEstimator): | |
| def __init__(self, base, residual): | |
| self.base = base | |
| self.residual = residual | |
| def fit(self, X, y): | |
| self.base.fit(X, y) | |
| self.residual.fit(X, y - self.base.predict(X)) |
| #!ipython | |
| # to use this run these two steps | |
| # curl https://gist.githubusercontent.com/tianhuil/a6675835a7a0c157fbcb296a743f52d4/raw/704a20201dd4362928f6e39ab0ab0bc0784b2af9/update_branches.ipy | |
| # ipython merged_branches.ipy | |
| !git checkout master | |
| branches = !git branch | |
| other_branches = [b.lstrip() for b in branches if b != '* master'] | |
| results = {} |
| # cd into a new folder and observe the error in the last line. | |
| yes | rm email.py* | |
| echo "from email.utils import formatdate; print 'OK'" > foo.py | |
| PYTHONPATH="" python foo.py | |
| touch email.py | |
| PYTHONPATH="" python foo.py |
React is a fairly simple browser-side framework. It promises to be faster than Angular.
The website itself, provides two great tutorials:
After going through these two, it is recommended that you go through this tutorial on react router:
| <!DOCTYPE html> | |
| <meta charset="utf-8"> | |
| <style> | |
| body { | |
| font: 10px sans-serif; | |
| } | |
| .axis path, | |
| .axis line { |
| #!/usr/bin/env python | |
| import sys | |
| n_rows = int(sys.argv[1]) | |
| data = map(lambda x: [], xrange(n_rows)) | |
| for k, line in enumerate(sys.stdin): | |
| data[k % n_rows] += [line.rstrip()] |
| import numpy as np | |
| from collections import namedtuple | |
| # Create a random dataset | |
| rng = np.random.RandomState(42) | |
| N_points = 10000 | |
| X = np.sort(5 * rng.rand(N_points, 1), axis=0) | |
| y = np.sin(X).ravel() | |
| y += .4 * (0.5 - rng.rand(N_points)) | |
| import numpy as np | |
| from collections import namedtuple | |
| # Create a random dataset | |
| rng = np.random.RandomState(42) | |
| N_points = 100 | |
| X = np.sort(5 * rng.rand(N_points, 1), axis=0) | |
| y = np.sin(X).ravel() | |
| y += .4 * (0.5 - rng.rand(N_points)) | |
| import numpy as np | |
| # Create a random dataset | |
| rng = np.random.RandomState(1) | |
| X = np.sort(5 * rng.rand(80, 1), axis=0) | |
| y = np.sin(X).ravel() | |
| y += .2 * (0.5 - rng.rand(80)) | |
| # Fit regression model | |
| from sklearn.tree import DecisionTreeRegressor |
| <!DOCTYPE html> | |
| <meta charset="utf-8"> | |
| <style> | |
| body { | |
| font: 10px sans-serif; | |
| } | |
| .axis path, | |
| .axis line { |