dojeda

from functools import partial

import matplotlib.pyplot as plt
import numpy as np
from pyriemann.utils.distance import distance_riemann


@partial(np.vectorize, excluded=['ref'])
def distance_riemann_v(c_xx, c_yy, ref):
    """ Vectorized version of distance_riemann projected in 2D

dojeda

openapi: "3.0.0"
info:
  version: 1.0.0
  title: Swagger Petstore
  license:
    name: MIT
servers:
  - url: http://petstore.swagger.io/v1
paths:
  /pets:

dojeda

""" Small proof of concept of an epoching function using NumPy strides

License: BSD-3-Clause
Copyright: David Ojeda <[email protected]>, 2018
"""

import numpy as np
from numpy.lib import stride_tricks

dojeda

#include <iostream>
#include <vector>
#include <Eigen/Dense>

int main(int argc, char *argv[]) {

    Eigen::MatrixXd X = Eigen::MatrixXd::Random(3, 3);

    // PCA = EVD of the covariance matrix
    Eigen::SelfAdjointEigenSolver < Eigen::MatrixXd > solver(X.selfadjointView<Eigen::Lower>());

dojeda

#include <iostream>
#include <vector>
#include <iterator>


using namespace std;

int main(int , char *[]) {

    vector<int> v1 = {0,1,2};

dojeda

#!/usr/bin/env python

def sqrt(x):
    
    begin = 0
    end   = x

    while begin <= end:

        mid = begin + (end - begin) // 2