rahulbhadani

// OPTION 1
class Object
{
    private:
        int* data;
    public:
        Object(const int size)
        { 
            data = new int[size]; 
        } // Acquisition

rahulbhadani

#include <string>
#include <functional>
#include <iostream>
#include <optional>

std::optional<bool> boolcar(std::string value) 
{
    if (value=="Toyota")
    {
        return true;

rahulbhadani

#include <string>
#include <functional>
#include <iostream>
#include <optional>


std::optional<std::string> getcar(std::string value) 
{
    if (value=="Toyota")
    {

rahulbhadani

#include <cstdint>
#include <iostream>
#include <limits>
#include <algorithm>
#include <vector>
#include <cassert>
#include <random>

using namespace std;

rahulbhadani

import scanpy as sc
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# change location of data here
folder = 'fig2(data7_check_10plots)/data1/'
file = '1g.csv'
count_df = pd.read_csv(folder + file)
count_df.set_index('Unnamed: 0', inplace=True)

rahulbhadani

import scanpy as sc
import pandas as pd


datafolder = "scRNAseqData/GSE146590"
adata = anndata.read_mtx(datafolder+'/GSM4396313_Ctrl_matrix.mtx.gz')
adata = adata1.T

adata.obs.index = pd.read_csv(datafolder+'/GSM4396313_Ctrl_barcodes.tsv.gz', header=None, index_col=0).index
genes = pd.read_csv(datafolder+'/GSM4396313_Ctrl_features.tsv.gz', header=None, index_col=0, sep='\t')[1].values

rahulbhadani

p = ot.unif(n_samples)
q = ot.unif(n_samples)

gw0, log0 = ot.gromov.gromov_wasserstein(
    C1, C2, p, q, 'square_loss', verbose=True, log=True)
print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))
pl.figure(1, (10, 5))
pl.imshow(gw0, cmap='jet')
pl.title('Gromov Wasserstein')
pl.colorbar()

rahulbhadani

C1 = sp.spatial.distance.cdist(xs, xs)
C2 = sp.spatial.distance.cdist(xt, xt)

C1 /= C1.max()
C2 /= C2.max()

pl.figure()
pl.subplot(121)
pl.imshow(C1)
pl.subplot(122)

rahulbhadani

import scipy as sp
import numpy as np
import matplotlib.pylab as pl
from mpl_toolkits.mplot3d import Axes3D  # noqa
import ot

n_samples = 50  # nb samples

mu_s = np.array([0, 0])

rahulbhadani

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

void polyfit(	const std::vector<double> &t,
		const std::vector<double> &v,
		std::vector<double> &coeff,
		int order