pavlin-policar

Moodle prepend lang tags where any translations are needed.

Long syntax:
Find:
(<span lang="es")
Replace:
<span lang="en" class="multilang">&nbsp;English version<\/span><span lang="sl" class="multilang">&nbsp;Slovenska verzija<\/span>$1

Short syntax:
Find:

pavlin-policar

# Solution found here: https://github.com/fchollet/keras/issues/4044

import h5py
f = h5py.File('model_file.h5', 'r+')
del f['optimizer_weights']
f.close()

pavlin-policar

import numpy as np


def entropy(p):
    """Compute the Shannon entropy of a distribution.

    The Shannon entropy is defined as follows
    :math:`\sum_x p(x_i) * \log p(x_i)`.

    Parameters

pavlin-policar

class SomeClass:
    class single_execution:
        """Compute property only once and cache the result for further access.
        When the property is first accessed, the result is computed, and the
        attribute on the instance is replaced with the result. This is
        essentially a single execution lazy property.
        """
        def __init__(self, method):
            self.__method = method
            self.__attribute_name = method.__name__

pavlin-policar

from functools import reduce
from typing import Iterable, Dict, List, Union, Optional
from collections import Counter

import numpy as np

from data_provider import get_talks


class TokenizedDocument(Counter):

pavlin-policar

import numpy as np

# Initialize the generating vector
x = np.array([1, 2, 3, 4, 5])
y = np.array([5, 4, 3, 2, 1])
# Initialize the permutation matrix
P = np.array([
    [0, 0, 0, 0, 1],
    [1, 0, 0, 0, 0],
    [0, 1, 0, 0, 0],

pavlin-policar

from typing import Callable

import numpy as np
import matplotlib.pyplot as plt


def lagrangian_interpolation(xs: np.ndarray, ys: np.ndarray) -> Callable:
    """Make a Lagrangian interpolating polynomial function."""
    def _interpolate(x: float) -> float:
        result = 0

pavlin-policar

class MaxHeap:
    def __init__(self, collection=None):
        self._heap = []

        if collection is not None:
            for el in collection:
                self.push(el)

    def push(self, value):
        self._heap.append(value)

pavlin-policar

read.h5ad = function (fname) {
  #' Read an H5AD file
  #' 
  #' @param fname str: The path to the H5AD file
  library(reticulate)
  library(Matrix)
  
  ad = import("anndata", convert = FALSE)
  sp = import("scipy.sparse", convert = FALSE)
  

pavlin-policar

"""Adapted from Orange 2"""

import numpy as np
import matplotlib.pyplot as plt


def compute_critical_difference(avg_ranks, N, alpha="0.05", type="nemenyi"):
    """ Returns critical difference for Nemenyi or Bonferroni-Dunn test
    according to given alpha (either alpha="0.05" or alpha="0.1") for average
    ranks and number of tested data sets N. Type can be either "nemenyi" for