Generate a random multivariate Gaussian sample.
506500
public double[] rand() {
        double[] spt = new double[mu.length];

        for (int i = 0; i < mu.length; i++) {
            double u, v, q;
            do {
                u = Math.random();
                v = 1.7156 * (Math.random() - 0.5);
                double x = u - 0.449871;
                double y = Math.abs(v) + 0.386595;
                q = x * x + y * (0.19600 * y - 0.25472 * x);
            } while (q > 0.27597 && (q > 0.27846 || v * v > -4 * Math.log(u) * u * u));

            spt[i] = v / u;
        }

        double[] pt = new double[sigmaL.nrows()];

        // pt = sigmaL * spt
        for (int i = 0; i < pt.length; i++) {
            for (int j = 0; j <= i; j++) {
                pt[i] += sigmaL.get(i, j) * spt[j];
            }
        }

        Math.plus(pt, mu);

        return pt;
    }
915894
def sample_cc(self, nsamples=1, weighted=True):
        
        weights = self.areas / np.sum(self.areas) if weighted else None
        for index in np.random.choice(a=len(self.geometries), size=nsamples, p=weights):
            yield self.geometries[index]
835894
def sample(self, nsamples=100, seed=None):
        
        if nsamples < 1:
            raise ValueError("Number of samples must be larger than 0, got '%g'" % nsamples)

        if seed is None:
            seed = random.randint(0, 2 ** 32)

        if self.mode == 'spark':
            result = asarray(self.values.tordd().values().takeSample(False, nsamples, seed))

        else:
            inds = [int(k) for k in random.rand(nsamples) * self.shape[0]]
            result = asarray([self.values[i] for i in inds])

        return self._constructor(result)
379393
def _gauss(mean: int, sigma: int) -> int:
        
        return int(random.gauss(mean, sigma))
579731
def make_random_MLdataset(max_num_classes = 20,
                          min_class_size = 20,
                          max_class_size = 50,
                          max_dim = 100,
                          stratified = True):
    ""

    smallest = min(min_class_size, max_class_size)
    max_class_size = max(min_class_size, max_class_size)
    largest = max(50, max_class_size)
    largest = max(smallest+3,largest)

    if max_num_classes != 2:
        num_classes = np.random.randint(2, max_num_classes, 1)
    else:
        num_classes = 2

    if type(num_classes) == np.ndarray:
        num_classes = num_classes[0]
    if not stratified:
        class_sizes = np.random.random_integers(smallest, largest, num_classes)
    else:
        class_sizes = np.repeat(np.random.randint(smallest, largest), num_classes)

    num_features = np.random.randint(min(3, max_dim), max(3, max_dim), 1)[0]
    # feat_names = [ str(x) for x in range(num_features)]

    class_ids = list()
    labels = list()
    for cl in range(num_classes):
        class_ids.append('class-{}'.format(cl))
        labels.append(int(cl))

    ds = MLDataset()
    for cc, class_ in enumerate(class_ids):
        subids = [ 's{}-c{}'.format(ix,cc) for ix in range(class_sizes[cc]) ]
        for sid in subids:
            ds.add_sample(sid, feat_generator(num_features), int(cc), class_)

    return ds
1197615
def sample(self, num_rows=1):
        
        self.check_fit()

        res = {}
        means = np.zeros(self.covariance.shape[0])
        size = (num_rows,)

        clean_cov = np.nan_to_num(self.covariance)
        samples = np.random.multivariate_normal(means, clean_cov, size=size)

        for i, (label, distrib) in enumerate(self.distribs.items()):
            cdf = stats.norm.cdf(samples[:, i])
            res[label] = distrib.percent_point(cdf)

        return pd.DataFrame(data=res)
1320133
def _sample(probabilities, population_size):
    """"""
    population = []
    for _ in range(population_size):
        solution = []
        for probability in probabilities:
            # probability of 1.0: always 1
            # probability of 0.0: always 0
            if random.uniform(0.0, 1.0) < probability:
                solution.append(1)
            else:
                solution.append(0)
        population.append(solution)
    return population
63534
def generate_state_data(means, weights):
    
    x_true = np.dot(means, weights)
    sample = np.random.poisson(x_true)
    return sample.astype(float)
1244175
def sample_niw(mu,lmbda,kappa,nu):
    '''
    
    '''
    # code is based on Matlab's method
    # reference: p. 87 in Gelman's Bayesian Data Analysis
    assert nu > lmbda.shape[0] and kappa > 0

    # first sample Sigma ~ IW(lmbda,nu)
    lmbda = sample_invwishart(lmbda,nu)
    # then sample mu | Lambda ~ N(mu, Lambda/kappa)
    mu = np.random.multivariate_normal(mu,lmbda / kappa)

    return mu, lmbda
97053
def generateRandomSDRVaryingSparsity(numSDR, numDims, minSparsity, maxSparsity,
                                     seed=42):
  
  randomSDRs = np.zeros((numSDR, numDims), dtype=uintType)
  indices = np.array(range(numDims))
  np.random.seed(seed)
  for i in range(numSDR):
    sparsity = np.random.random() * (maxSparsity - minSparsity) + minSparsity
    numActiveInputBits = int(sparsity * numDims)
    randomIndices = np.random.permutation(indices)
    activeBits = randomIndices[:numActiveInputBits]
    randomSDRs[i, activeBits] = 1
  return randomSDRs
394095
def randsample(vec, nr_samples, with_replacement = False):
    
    if not with_replacement:
        return np.random.permutation(vec)[0:nr_samples]
    else:
        return np.asarray(vec)[np.random.randint(0, len(vec), nr_samples)]
910013
def sample(self, n=100, seed=None):
        
        if n < 1:
            raise ValueError("Number of samples must be larger than 0, got '%g'" % n)

        if seed is None:
            seed = random.randint(0, 2 ** 32)

        if self.mode == 'spark':
            result = asarray(self.values.tordd().values().takeSample(False, n, seed))

        else:
            basedims = [self.shape[d] for d in self.baseaxes]
            inds = [unravel_index(int(k), basedims) for k in random.rand(n) * prod(basedims)]
            result = asarray([self.values[tupleize(i) + (slice(None, None),)] for i in inds])

        return self._constructor(result, index=self.index)
52361
def random_samples(self, n):
        
        samples = self.samples
        inds = rand.randint(len(samples),size=int(n))

        newsamples = samples.iloc[inds]
        newsamples.reset_index(inplace=True)
        return newsamples
899992
def samples(self, gp, Y_metadata=None, **kw):
        
        orig_shape = gp.shape
        gp = gp.flatten()
        N = Y_metadata['trials']
        Ysim = np.random.binomial(N, self.gp_link.transf(gp))
        return Ysim.reshape(orig_shape)
719934
def _random_helper(random, sampler, params, shape, dtype, ctx, out, kwargs):
    """"""
    if isinstance(params[0], NDArray):
        for i in params[1:]:
            assert isinstance(i, NDArray), \
                "Distribution parameters must all have the same type, but got " \
                "both %s and %s."%(type(params[0]), type(i))
        return sampler(*params, shape=shape, dtype=dtype, out=out, **kwargs)
    elif isinstance(params[0], numeric_types):
        if ctx is None:
            ctx = current_context()
        if shape is _Null and out is None:
            shape = 1
        for i in params[1:]:
            assert isinstance(i, numeric_types), \
                "Distribution parameters must all have the same type, but got " \
                "both %s and %s."%(type(params[0]), type(i))
        return random(*params, shape=shape, dtype=dtype, ctx=ctx, out=out, **kwargs)

    raise ValueError("Distribution parameters must be either NDArray or numbers, "
                     "but got %s."%type(params[0]))
844439
def make_data(n,m):
    """"""
    I = range(1,n+1)
    J = range(1,m+1)
    x,y,w = {},{},{}
    for i in I:
        x[i] = random.randint(0,100)
        y[i] = random.randint(0,100)
        w[i] = random.randint(1,5)
    return I,J,x,y,w
1000026
public double[] sample(int numSamples, Random rand)
    {
        double[] samples = new double[numSamples];
        for(int i = 0; i < samples.length; i++)
            samples[i] = invCdf(rand.nextDouble());
        
        return samples;
    }