wassname

def smooth_noise(shp, dim=1, smoothing=30):
    """
    Generate smoothed random noise that looks like a random walk.
    
    - shp: shape of noise
    - dim: dimension to smooth along
    - smooth_steps: smoothing steps
    

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from torch import nn
import torch
import torch.nn.functional as F
import numpy as np



def radneg2radpos(f):
    """convert radians in range [-pi,pi] to [0,2*pi]"""
    return np.where(f < 0, f + np.pi * 2, f)

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import pandas as pd
import numpy as np
import hashlib
import json

def default(o):
    """Sets are unordered so are no good for hasing"""
    if isinstance(o, set):
        try:
            o = sorted(o)

wassname

%pylab inline
import pandas as pd
import dask.dataframe as dd

def get_unbal_df(size = 100, balance=None):
    """Get a randomly unbalanced df"""
    if balance is None:
        balance = np.random.randint(-100, 100)
    
    if balance<0:

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from typing import Iterator, Collection
from fastai.data_block import CategoryListBase
from fastai.text import *


class BinaryProcessor(CategoryProcessor):
    def create_classes(self, classes):
        self.classes = classes
        if classes is not None: self.c2i = {0:0, 1:1}
    def generate_classes(self, items):

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"""
Cache a torch dataset to npy files using dask

url:https://gist.github.com/wassname/f38f8774b6f97977b660d20dfa0f0036
lic:MIT
author:wassname

usage:    
    batch_size=16
    chunk_size=batch_size*4

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#!/usr/bin/env python
# coding: utf-8
get_ipython().run_line_magic('pylab', 'inline')
import torch

def jaccard_distance_loss(y_true, y_pred, smooth=100):
    """
    Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)
            = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|))
    

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def cache_load_utturances(ttl=360000):
    """
    Decorator for wrapping simple cache around load_utterances.

    Since some arguments are unhashable (tokenizer) or immutable (list) we need to make the key manually
    """

    def decorate(func):
        @simple_cache.wraps(func)
        def wrapper(**kwargs):

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"""
@url: https://gist.github.com/wassname/f3cbdc14f379ba9ec2acfafe5c1db592
"""
import pandas as pd
import sklearn.metrics
import numpy as np

def classification_report(*args, **kwargs):
    """
    

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import numpy as np 

# handle pytorch tensors etc, by using tensorboardX's method
try:
    from tensorboardX.x2num import make_np
except ImportError:
    def make_np(x):
        return np.array(x).copy().astype('float16')

class RunningStats(object):