alik604

def EEGNet(nb_classes, Chans = 64, Samples = 128, 
             dropoutRate = 0.5, kernLength = 64, F1 = 8, 
             D = 2, F2 = 16, norm_rate = 0.25, dropoutType = 'Dropout'):
    """ Keras Implementation of EEGNet
    http://iopscience.iop.org/article/10.1088/1741-2552/aace8c/meta

    Inputs:
        
      nb_classes      : int, number of classes to classify
      Chans, Samples  : number of channels and time points in the EEG data

alik604

import pylab as plt
import numpy as np

import matplotlib
import matplotlib.pyplot as plt

import keras
from keras.models import Sequential, Model
from keras.layers import Dense
from keras.optimizers import Adam

alik604

# https://github.com/alik604/Notebooks/blob/master/Data%20Science-Datasets/MNIST/Where_to_add_GaussianNoise_MNIST_.ipynb
# data - MNIST
# note - test_size=0.98... ya...
model = Sequential()
model.add(Conv2D(filters=100, kernel_size=3)) # remove relu
# model.add(GaussianNoise(0.5)) # sandwich between
# GaussianNoise(0.5) here - accuracy: 0.9829 - val_loss: 0.4050 - val_accuracy: 0.9024
model.add(Activation('relu')) # add relu 
# GaussianNoise(0.5) here - accuracy: 0.9900 - val_loss: 1.4703 - val_accuracy: 0.8073
model.add(MaxPooling2D(pool_size=(2, 2)))

alik604

# @credit - kaggle.com/hanzh0420/image-augmentation-with-opencv
import os
print(os.listdir("../input"))
# Input data files are available in the "../input/" directory.
# Any results you write to the current directory are saved as output.

import numpy as np 
#import pandas as pd # pd.read_csv
import cv2
import random

alik604

alik604

%%capture
!pip install scikit-plot
!pip install catboost
!pip install mlxtend
!pip install yfinance
!pip install pyod

import pyod
import yfinance
import xgboost  # xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBClassifier

alik604

import urllib.parse as parse

url = ""
if 'url' not in globals():
    print("Enter URL to parse")
    url = input()


def printDict(data):
    for x in data:

alik604

!pip install mnist
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd 
from sklearn import metrics
import mnist

# from hmmlearn.hmm import GaussianHMM, MultinomialHMM

X_train = mnist.train_images()

alik604

# make dataset to input
def build_dataset(time_series, seq_length):
    dataX = []
    dataY = []
    for i in range(0, len(time_series) - seq_length):
        _x = time_series[i:i + seq_length, :]
        _y = time_series[i + seq_length, [-1]]  # Next close price
        print(_x, "->", _y)
        dataX.append(_x)
        dataY.append(_y)

alik604

import numpy as np
import pylab as pl
from numpy import fft


def fourierExtrapolation(x, n_predict):
    n = x.size
    n_harm = 10                     # number of harmonics in model
    t = np.arange(0, n)
    p = np.polyfit(t, x, 1)         # find linear trend in x