jnothman

from __future__ import print_function
import numpy as np
from sklearn.metrics import precision_recall_fscore_support as prfs, confusion_matrix
from sklearn.preprocessing import label_binarize
true = [0, 0, 0, 1, 1, 2]
preds = [('under-generate 1', [0, 0, 0, 0, 1, 2]),
         ('under-generate 2', [0, 0, 0, 1, 1, 0]),
         ('over-generate 1', [0, 1, 1, 1, 1, 2]),
         ('confuse 1 and 2', [0, 0, 0, 1, 2, 1])]

jnothman

#!/usr/bin/env python
"""
Faced with a collection of JSON blobs, this script lists what
paths (i.e. sequences of nested keys) exist in the data from
root to leaf.

For example:
  $ echo '[{"a": {"a1": 124}, "b": 111}, {"a": {"a2": 111}, "c": null}]' \
      | list-json-paths.py
will output:

jnothman

from abc import ABCMeta, abstractmethod

from .base import BaseEstimator
from .externals.six import iteritems, with_metaclass


class BaseParameterTranslator(with_metaclass(ABCMeta, BaseEstimator)):

    @property
    def fit(self):

jnothman

from __future__ import print_function, division
import numpy as np

from sklearn.base import BaseEstimator
from sklearn.cluster import MiniBatchKMeans, SpectralClustering
from sklearn.neighbors import KNeighborsClassifier
from sklearn.utils.random import sample_without_replacement
from sklearn.svm import OneClassSVM
from sklearn.linear_model import LogisticRegression
from sklearn import datasets

jnothman

#!/usr/bin/env python
"""Count or sum, while uniquing rows, without full sort of data

By using --key-fields, can also show example row that has some particular fields.

(This was much simpler when it just counted!)
"""

import sys
import argparse

jnothman

from sklearn.base import BaseEstimator, Clone
from sklearn.utils import safe_mask
class ModelByCluster(BaseEstimator):
    def __init__(self, clusterer, estimator):
        self.clusterer = clusterer
        self.estimator = estimator
    def fit(self, X, y):
        self.clusterer_ = clone(self.clusterer)
        clusters = self.clusterer_.fit_predict(X)
        n_clusters = len(np.unique(clusters))

jnothman

cdef cppclass myclass[T]:
    T x
    
    object foo(self):
        return self.x


from cython cimport floating

def get(floating x):

jnothman

#!/bin/bash
jq -s 'add | map({user: .user.login,
                  milestone: .milestone.title,
                                  labels: .labels | map(.name) | join("|"),
                  created_at,
                  closed_at,
                  updated_at,
                  state,
                  comments,
                  title,

jnothman

javascript:
var status_lists = document.getElementsByClassName('merge-status-list');
var ci_links = status_lists[status_lists.length - 1].getElementsByClassName('status-actions');
for (var i in ci_links) {
    var ci_link = ci_links[i];
    if (ci_link.tagName.toUpperCase() != 'A') {
        ci_link = ci_link.getElementsByTagName('a')[0];
    }
    var url = ci_link.getAttribute('href');
    var match = /circleci.com\/.*?([0-9]+)\?/.exec(url);

jnothman

import numpy as np
from scipy import sparse


def flexible_concatenate(it, final_len=None):
    """Concatenate the elements of an iterable

    Supports generators of arrays, lists, sparse matrices or tuples thereof

    >>> import numpy as np