Best value of entropy
972000
public void mainFindBestValEntropy(Node root) {
		if (root != null) {
			DoubleVector parentClassCL = new DoubleVector();
			DoubleVector classCountL = root.classCountsLeft; //class count left
			DoubleVector classCountR = root.classCountsRight; //class count left
			double numInst = root.classCountsLeft.sumOfValues() + root.classCountsRight.sumOfValues();
			double classCountLSum = root.classCountsLeft.sumOfValues();
			double classCountRSum = root.classCountsRight.sumOfValues();
			double classCountLEntropy = entropy(classCountL);
			double classCountREntropy = entropy(classCountR);
			this.minEntropyTemp = ( classCountLSum / numInst) * classCountLEntropy 
					+ (classCountRSum / numInst)* classCountREntropy;
			for (int f = 0; f < root.classCountsLeft.numValues(); f++) {
				parentClassCL.setValue(f, root.classCountsLeft.getValue(f));
				}
			findBestValEntropy(root ,classCountL , classCountR, true, this.minEntropyTemp, parentClassCL);
			}
		}
868848
def shannon_entropy(time_series):
    

    # Check if string
    if not isinstance(time_series, str):
        time_series = list(time_series)

    # Create a frequency data
    data_set = list(set(time_series))
    freq_list = []
    for entry in data_set:
        counter = 0.
        for i in time_series:
            if i == entry:
                counter += 1
        freq_list.append(float(counter) / len(time_series))

    # Shannon entropy
    ent = 0.0
    for freq in freq_list:
        ent += freq * np.log2(freq)
    ent = -ent
    return ent
941994
def entropy_H(self, data):
        """"""

        if not data:
            return 0.0

        occurences = Counter(bytearray(data))

        entropy = 0
        for x in occurences.values():
            p_x = float(x) / len(data)
            entropy -= p_x*math.log(p_x, 2)

        return entropy
82646
function (paranoia) {
    var entropyRequired = this._PARANOIA_LEVELS[ (paranoia !== undefined) ? paranoia : this._defaultParanoia ];

    if (this._strength && this._strength >= entropyRequired) {
      return (this._poolEntropy[0] > this._BITS_PER_RESEED && (new Date()).valueOf() > this._nextReseed) ?
        this._REQUIRES_RESEED | this._READY :
        this._READY;
    } else {
      return (this._poolStrength >= entropyRequired) ?
        this._REQUIRES_RESEED | this._NOT_READY :
        this._NOT_READY;
    }
  }
1205623
def entropy(dictionary):
    
    total = 0.0
    entropy = 0
    for key in dictionary.keys():
        total += dictionary[key]

    for key in dictionary.keys():
        entropy += dictionary[key]/total * log(1.0/(dictionary[key]/total), 2)
    return entropy
977561
def entropy_bits(
        lst: Union[
            List[Union[int, str, float, complex]],
            Tuple[Union[int, str, float, complex]]
        ]
) -> float:
    """"""
    # Based on https://stackoverflow.com/a/45091961
    if not isinstance(lst, (list, tuple)):
        raise TypeError('lst must be a list or a tuple')

    for num in lst:
        if not isinstance(num, (int, str, float, complex)):
            raise TypeError('lst can only be comprised of int, str, float, '
                            'complex')

    n_lst = len(lst)

    if n_lst <= 1:
        return 0.0

    # Some NumPy replacements
    counts = [lst.count(x) for x in lst]
    probs = [c / n_lst for c in counts]

    # Compute entropy
    entropy = 0.0
    for prob in probs:
        entropy -= prob * log2(prob)

    return entropy
372779
def best(self):
        '''
        
        '''

        if len(self.choices) < 1:
            print('slots: No trials run so far.')
            return None
        else:
            return np.argmax(self.wins/(self.pulls+0.1))
270377
def prox_max_entropy(X, step, gamma=1):
    
    from scipy.special import lambertw
    gamma_ = _step_gamma(step, gamma)
    # minimize entropy: return gamma_ * np.real(lambertw(np.exp((X - gamma_) / gamma_) / gamma_))
    above = X > 0
    X[above] = gamma_ * np.real(lambertw(np.exp(X[above]/gamma_ - 1) / gamma_))
    return X
1140888
def _adjust_probability_vec_best(population, fitnesses, probability_vec,
                                 adjust_rate):
    """"""
    best_solution = max(zip(fitnesses, population))[1]

    # Shift probabilities towards best solution
    return _adjust(probability_vec, best_solution, adjust_rate)
972085
def words_amount_needed(entropybits: Union[int, float],
                        entropy_w: Union[int, float],
                        entropy_n: Union[int, float],
                        amount_n: int) -> int:
    """"""
    # Thanks to @julianor for this tip to calculate default amount of
    # entropy: minbitlen/log2(len(wordlist)).
    # I set the minimum entropy bits and calculate the amount of words
    # needed, cosidering the entropy of the wordlist.
    # Then: entropy_w * amount_w + entropy_n * amount_n >= ENTROPY_BITS_MIN

    if not isinstance(entropybits, (int, float)):
        raise TypeError('entropybits can only be int or float')
    if not isinstance(entropy_w, (int, float)):
        raise TypeError('entropy_w can only be int or float')
    if not isinstance(entropy_n, (int, float)):
        raise TypeError('entropy_n can only be int or float')
    if not isinstance(amount_n, int):
        raise TypeError('amount_n can only be int')
    if entropybits < 0:
        raise ValueError('entropybits should be greater than 0')
    if entropy_w <= 0:
        raise ValueError('entropy_w should be greater than 0')
    if entropy_n < 0:
        raise ValueError('entropy_n should be greater than 0')
    if amount_n < 0:
        raise ValueError('amount_n should be greater than 0')

    amount_w = (entropybits - entropy_n * amount_n) / entropy_w

    if amount_w > -1.0:
        return ceil(fabs(amount_w))

    return 0
607077
def adjusted_entropy(entropy_threshhold: 0)
      revpassword = base_password.reverse
      min_entropy = [EntropyCalculator.calculate(base_password), EntropyCalculator.calculate(revpassword)].min
      QWERTY_STRINGS.each do |qwertystr|
        qpassword = mask_qwerty_strings(base_password, qwertystr)
        qrevpassword = mask_qwerty_strings(revpassword, qwertystr)
        if qpassword != base_password
          numbits = EntropyCalculator.calculate(qpassword)
          min_entropy = [min_entropy, numbits].min
          return min_entropy if min_entropy < entropy_threshhold
        end
        if qrevpassword != revpassword
          numbits = EntropyCalculator.calculate(qrevpassword)
          min_entropy = [min_entropy, numbits].min
          return min_entropy if min_entropy < entropy_threshhold
        end
      end
      min_entropy
    end
1267985
function entropy(str) {
    if (!str) return 0;
    return Math.round(str.length * (Math.log(alphabetSize(str)) / Math.log(2)));
}
647707
function entropy(labels, ix) {
    var N= ix.length;
    var p=0.0;
    for(var i=0;i<N;i++) {
        if(labels[ix[i]]==1) p+=1;
    }
    p=(1+p)/(N+2); // let's be bayesian about this
    q=(1+N-p)/(N+2);
    return (-p*Math.log(p) -q*Math.log(q));
}
1327198
def entropy(self, logits):
        
        probs = torch.exp(logits)
        entropy = - torch.sum(probs * logits, dim=-1)
        return entropy
1289302
def generate_entropy(strength, internal_entropy, external_entropy):
    '''
    
    '''
    if strength not in (128, 192, 256):
        raise Exception("Invalid strength")

    if not internal_entropy:
        raise Exception("Internal entropy is not provided")

    if len(internal_entropy) < 32:
        raise Exception("Internal entropy too short")

    if not external_entropy:
        raise Exception("External entropy is not provided")

    if len(external_entropy) < 32:
        raise Exception("External entropy too short")

    entropy = hashlib.sha256(internal_entropy + external_entropy).digest()
    entropy_stripped = entropy[:strength // 8]

    if len(entropy_stripped) * 8 != strength:
        raise Exception("Entropy length mismatch")

    return entropy_stripped
500359
private double calcEntropy(final List<Match> matches, final boolean include_brute_force)
    {
        double entropy = 0;
        for (Match match : matches)
        {
            if (include_brute_force || !(match instanceof BruteForceMatch))
            {
                entropy += match.calculateEntropy();
            }
        }
        return entropy;
    }
139177
def _get_image_entropy(self, image):
        """"""
        hist = image.histogram()
        hist_size = sum(hist)
        hist = [float(h) / hist_size for h in hist]
        return -sum([p * math.log(p, 2) for p in hist if p != 0])