raphaelvallat

import numpy as np
import multiprocessing
from joblib import Parallel, delayed
from scipy.spatial.distance import pdist, squareform

def _dcorr(y, n2, A, dcov2_xx):
    """Helper function for distance correlation bootstrapping.
    """
    # Pairwise Euclidean distances
    b = squareform(pdist(y, metric='euclidean'))

raphaelvallat

'''
Non-parametric computation of entropy and mutual-information

Adapted by G Varoquaux for code created by R Brette, itself
from several papers (see in the code).

These computations rely on nearest-neighbor statistics
'''
import numpy as np

raphaelvallat

raphaelvallat

import numpy as np

def find_closest_divisor(n, m): 
    """Find the divisor of n closest to m
    """
    divisors = np.array([ i for i in range(1, int(np.sqrt(n)+1)) if n % i == 0 ])
    divisions = n / divisors 
    return divisions[np.argmin(np.abs(m - divisions))]

number, divisor = 1024, 100

raphaelvallat

library(ez)
library(lsmeans)
library(effsize)

# Load the file
df <- read.csv(file="", head=TRUE, sep=",")

# Choose dependant variable (e.g. reaction time)
df$dv <- df$RT