larsbratholm

#!/usr/bin/env bash

export BOOST_ROOT_DIR=/home/lab/opt/boost-1.58_gcc-1.61
export BOOST_ROOT=$BOOST_ROOT_DIR
export LD_LIBRARY_PATH=$BOOST_ROOT_DIR/lib #:/usr/lib64:$LD_LIBRARY_PATH

export CC=gcc-6; export CXX=g++-6

mkdir $1
cd $1

larsbratholm

def dice_example_l():
    """
    Example with a biased dice with a previously observed mean
    The lagrange multiplier l is optimized rather than p
    """
    def entropy(l, *args):
        """
        Entropy of p
        """
        p = analytical(l)

larsbratholm

if metric == 'rmse':
    metric_fun = lambda x: np.std(x, axis=1)
elif metric == 'mae':
    metric_fun = lambda x: np.mean(abs(x), axis=1)

larsbratholm

tokens = line.split()

try: 
    atom = tokens[0].upper()
    numbers = np.asarray(tokens[1:4], dtype=float)
except:
    exit("Reading the .xyz file failed in line {0}. Please check the format.".format(lines_read + 2))

V.append(numbers)
atoms.append(atom)

larsbratholm

import scipy.stats
import matplotlib.pyplot as plt
import sklearn.model_selection
import sklearn.neighbors

def kde_scipy(x, x_grid):
    """
    Using heuristic for bandwidth
    """
    kde = scipy.stats.gaussian_kde(x)

larsbratholm

15

N -0.1805406906 1.4242349529 -0.0108675046
C 0.0495047193 0.0416229792 0.0372221139
N -1.0209269792 -0.7454454085 0.1001912635
H -1.9045501495 -0.2501680795 0.1747475728
C -1.1035482774 -2.0851342296 0.0629959263
C -2.1858877347 -2.9591823159 0.1129706520
N -1.7728257655 -4.2139610664 0.0327337682
C -0.3907063555 -4.1580119412 -0.069503388

larsbratholm

import sklearn.neighbors
import sklearn.ensemble
import itertools

def create_lookup_table(angles, cs, lookup_table_filename, granularity_degree=20):
    """
    Creates lookup tables for procs15
    """

    # convert everything to numpy arrays

larsbratholm

import numpy as np

if __name__ == "__main__":
  # parse angles to get an array like
  x = [
    [160, 90],
    [80, 120]
  ]
  
  # save the parsed data

larsbratholm

for i in range(100000):
    # get 6 random angles
    ang1, ang2, ang3, ang4, ang5, ang6 = np.random.randint(0,360,size=6)
    # set angles here
    # write gaussian constrained optimization (PM6) where all the angles are part of the filename

larsbratholm

import numpy as np
import matplotlib.pyplot as plt

if __name__ == "__main__":
    w = 0.41
    N2 = 2.4
    x = np.linspace(0.5,6,1000)
    s2 = np.log(1+w/x**2)
    s = np.sqrt(s2)
    mu = np.log(x/np.sqrt(1+w/x**2))