usmcamp0811

import pandas as pd
import numpy as np

def bin_data(dataframe, field, num_bins):
    bins = np.linspace(df[field].min(), df[field].max(), num_bins)
    dataframe[field+'_Bins'] = pd.cut(dataframe[field], bins)
    return dataframe

if __name__ == "__main__":
    df = pd.DataFrame(np.random.uniform(0, 100, size=(100, 3)))

usmcamp0811

import pandas as pd
import numpy as np


def is_outlier(value, p25, p75):
    """Check if value is an outlier
    """
    lower = p25 - 1.5 * (p75 - p25)
    upper = p75 + 1.5 * (p75 - p25)
    return value <= lower or value >= upper

usmcamp0811

import tensorflow as tf
import pandas as pd
import numpy as np
from tqdm import tqdm
import os


def _int64_feature(value):
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

usmcamp0811

import tensorflow as tf
import pandas as pd
import numpy as np
import os
from tqdm import tqdm
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
from PIL import Image, ImageOps
import PIL

usmcamp0811

import pandas as pd
import numpy as np
import time
import random

'''
This class will return a random time series based window of data. It intakes a dataframe
that is already pre sorted into the correct time pattern where index 0 is timestep 0
and index N is timestep N. The function window_maker will incrementally step through
the entire dataframe returning the next window of data. The function random_window_maker

usmcamp0811

def text_class_encoder(df):
    dtypes = pd.DataFrame(df.dtypes)
    text_cols = list(dtypes[dtypes.iloc[:,0] == 'object'].index)
    label_encoder_dict = {}
    for col in text_cols:
        label_encoder_dict[col] = LabelEncoder()
        label_encoder_dict[col].fit(df[col])
        df[col] = label_encoder_dict[col].transform(df[col])
    return df, label_encoder_dict

usmcamp0811

import numpy as np

class batcher(object):
    def __init__(self, data, batch_size, target=None):
        self.data = data
        self.batch_size = batch_size
        self.batch_n = 0
        self.n_batches =int(data.shape[0]/batch_size)
        self.target = target
        if target != None:

usmcamp0811

import scipy.io as spio


def loadmat(filename):
    '''
    this function should be called instead of direct spio.loadmat
    as it cures the problem of not properly recovering python dictionaries
    from mat files. It calls the function check keys to cure all entries
    which are still mat-objects
    '''

usmcamp0811

import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from sklearn import datasets
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import StratifiedKFold
#%matplotlib qt
from sklearn.cluster import KMeans
import numpy as np
from sklearn.manifold import TSNE

usmcamp0811

from sklearn.manifold import TSNE
from mpl_toolkits.mplot3d import Axes3D
%matplotlib qt
from IPython import display
import matplotlib.cm as cmx
import matplotlib.colors as colors

def get_cmap(N):
    '''Returns a function that maps each index in 0, 1, ... N-1 to a distinct 
    RGB color.'''