def __init__(self, cases=[1], K=[5], n_total=[1000], n_samples=[[5, 5]], b_true=[None], w_true=[None], num_results=[10e3], baseline_index=None, formula="x_0"): """ constructor. Simulated Parameters are passed to the constructor as lists, except the type of model, which is fixed for all simulations. The simulation is carried out over all possible combinations of specified parameters. See the default values for examples Parameters ---------- cases -- List Number of (binary) covariates K -- List Number of cell types n_total -- List number of cells per sample n_samples -- List number of samples. Each sublist specifies the number of samples for each covariate combination, length 2**cases b_true -- List Base composition. Each sublist has dimension K. w_true -- List Effect composition. Each sublist is a nested list that represents a DxK effect matrix num_results -- List MCMC chain length baseline_index -- int Index of reference cellltype (None for no baseline) formula -- str R-style formula used in model specification """ # HMC Settings self.n_burnin = int(5e3) # number of burn-in steps self.step_size = 0.01 self.num_leapfrog_steps = 10 # All parameter combinations self.simulation_params = list( itertools.product(cases, K, n_total, n_samples, b_true, w_true, num_results)) # Setup result objects self.mcmc_results = {} self.parameters = pd.DataFrame({ 'cases': [], 'K': [], 'n_total': [], 'n_samples': [], 'b_true': [], 'w_true': [], 'num_results': [] }) self.baseline_index = baseline_index self.formula = formula np.set_seed(1234)
# limitations under the License. from __future__ import print_function import unittest import numpy import paddle.fluid as fluid import paddle.fluid.layers as layers import paddle.fluid.core as core from paddle.fluid.contrib.layers import BasicLSTMUnit from paddle.fluid.executor import Executor from paddle.fluid import framework import numpy as np np.set_seed(123) SIGMOID_THRESHOLD_MIN = -40.0 SIGMOID_THRESHOLD_MAX = 13.0 EXP_MAX_INPUT = 40.0 def sigmoid(x): y = np.copy(x) y[x < SIGMOID_THRESHOLD_MIN] = SIGMOID_THRESHOLD_MIN y[x > SIGMOID_THRESHOLD_MAX] = SIGMOID_THRESHOLD_MAX return 1. / (1. + np.exp(-y)) def tanh(x): y = -2. * x
# %% plt.plot() film_deaths.IMDB_Rating.hist(bins=10) plt.xlabel('IMBD_Rating') plt.ylabel('Films Count') plt.show() # %% imdb_mean = film_deaths.IMDB_Rating.mean() imdb_std = film_deaths.IMDB_Rating.std() print(f'IMDB mean = {imdb_mean:.3}, std = {imdb_std:.3}') # %% np.set_seed(42) film_deaths['imdb_simulation'] = np.random.normal(loc=imdb_mean, scale=imdb_std, size=len(film_deaths)) plt.plot() film_deaths.imdb_simulation.hist(bins=10) plt.xlabel('IMDB_Rating (Simulation)') plt.ylabel('Films Count (Simulation)') plt.show() # %% from scipy.stats import probplot