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import numpy as np
import pandas as pd
import tensorflow as tf

tf.enable_eager_execution()

training_df: pd.DataFrame = pd.DataFrame(
    data={
        'feature1': np.random.rand(10),
        'feature2': np.random.rand(10),

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import numpy as np
import pandas as pd
from scipy.interpolate import CubicSpline


def interpolate_time_series(time_series: pd.Series, n_points: int) -> pd.Series:
    """
    Interpolate the pattern in the standardized time series data into `n_points`
    number of values at equal intervals between 0 and 1.

minesh1291

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from keras.callbacks import TensorBoard

batch_size = 128

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# You can change the directory name
LOG_DIR = 'tb_logs'

!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
!unzip ngrok-stable-linux-amd64.zip

import os
if not os.path.exists(LOG_DIR):
  os.makedirs(LOG_DIR)
  

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# Which file to send?
file_name = "REPO.tar"

from googleapiclient.http import MediaFileUpload
from googleapiclient.discovery import build

auth.authenticate_user()
drive_service = build('drive', 'v3')

def save_file_to_drive(name, path):

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name: release
on:
  push:
    branches:
    - master

jobs:
  release:
    name: Build
    runs-on: ubuntu-latest

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#!/usr/bin/env python3

import os
import sys
from glob import glob
import librosa
import time
import numpy

numpy.random.seed(42)

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# The script illustartes stunning difference on my machine with processing of signal with multiprocessing and joblib. 
# The slowness of multiprocessing is likely caused by oversubscription 

import time
import numpy as np
import librosa
from joblib import Parallel, delayed
from functools import partial
from multiprocessing import Pool

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from sklearn.utils import check_arrays

def mean_absolute_percentage_error(y_true, y_pred): 
    """
    Use of this metric is not recommended; for illustration only. 
    See other regression metrics on sklearn docs:
      http://scikit-learn.org/stable/modules/classes.html#regression-metrics

    Use like any other metric
    >>> y_true = [3, -0.5, 2, 7]; y_pred = [2.5, -0.3, 2, 8]