maidens

from __future__ import print_function, division

from math import sqrt

from scipy.special import ndtri

def _proportion_confidence_interval(r, n, z):
    """Compute confidence interval for a proportion.
    
    Follows notation described on pages 46--47 of [1]. 

samoshkin

bind -T root F12  \
  set prefix None \;\
  set key-table off \;\
  set status-style "fg=$color_status_text,bg=$color_window_off_status_bg" \;\
  set window-status-current-format "#[fg=$color_window_off_status_bg,bg=$color_window_off_status_current_bg]$separator_powerline_right#[default] #I:#W# #[fg=$color_window_off_status_current_bg,bg=$color_window_off_status_bg]$separator_powerline_right#[default]" \;\
  set window-status-current-style "fg=$color_dark,bold,bg=$color_window_off_status_current_bg" \;\
  if -F '#{pane_in_mode}' 'send-keys -X cancel' \;\
  refresh-client -S \;\

bind -T off F12 \

fsausset

from keras.models import Sequential
from keras.models import model_from_json
from keras import backend as K
import tensorflow as tf
from tensorflow.python.tools import freeze_graph
import os

# Load existing model.
with open("model.json",'r') as f:
    modelJSON = f.read()

simonkamronn

from scipy.stats import norm, shapiro, kstest, anderson
import bokeh.plotting as bplt
from bokeh import layouts
from bokeh.charts import Histogram, Scatter
from bokeh.models import Span
import pandas as pd
import numpy as np


def vertical_histogram(y):

wrwrwr

#!/usr/bin/env bash

# Path to the project directory (that should include requirements.txt),
# Files and directories within that need to be deployed.
project=../backend
contents=(module lamdba_handler.py)

# Unnecessary parts. Note that there are some inter-dependencies in SciPy,
# for example to use scipy.stats you also need scipy.linalg, scipy.integrate,
# scipy.misc, scipy.sparse, and scipy.special.

wassname

"""
A weighted version of categorical_crossentropy for keras (2.0.6). This lets you apply a weight to unbalanced classes.
@url: https://gist.github.com/wassname/ce364fddfc8a025bfab4348cf5de852d
@author: wassname
"""
from keras import backend as K
def weighted_categorical_crossentropy(weights):
    """
    A weighted version of keras.objectives.categorical_crossentropy
    

juanpabloaj

Total of pip packages downloaded separated by Python versions

From June 26, 2016 (python 3.5.2 release) to Aug. 31, 2016.

Python versions from 2.6 to 3.5

Without 2.7

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

saliksyed

""" Deep Auto-Encoder implementation
	
	An auto-encoder works as follows:

	Data of dimension k is reduced to a lower dimension j using a matrix multiplication:
	softmax(W*x + b)  = x'
	
	where W is matrix from R^k --> R^j

	A reconstruction matrix W' maps back from R^j --> R^k