Python rvs Examples

Example #1

def build_toy_dataset(N, noise_std=0.1):
  x = np.concatenate([np.linspace(0, 2, num=N / 2),
                      np.linspace(6, 8, num=N / 2)])
  y = 0.075 * x + norm.rvs(0, noise_std, size=N)
  x = (x - 4.0) / 4.0
  x = x.reshape((N, 1))
  return x, y

Example #2

File: bayesian_linear_regression_ppc.py Project: maedoc/edward

def build_toy_dataset(N, noise_std=0.5):
    X = np.concatenate(
        [np.linspace(0, 2, num=N / 2),
         np.linspace(6, 8, num=N / 2)])
    y = 2.0 * X + 10 * norm.rvs(0, noise_std, size=N)
    X = X.reshape((N, 1))
    return X.astype(np.float32), y.astype(np.float32)

Example #3

File: bayesian_nn.py Project: xsongx/edward

def build_toy_dataset(N=40, noise_std=0.1):
    ed.set_seed(0)
    D = 1
    x = np.concatenate([np.linspace(0, 2, num=N / 2), np.linspace(6, 8, num=N / 2)])
    y = np.cos(x) + norm.rvs(0, noise_std, size=N)
    x = (x - 4.0) / 4.0
    x = x.reshape((N, D))
    return {"x": x, "y": y}

Example #4

File: bayesian_linear_regression_plot.py Project: franciscojavierarceo/edward

def build_toy_dataset(N=40, noise_std=0.1):
    ed.set_seed(0)
    x  = np.concatenate([np.linspace(0, 2, num=N/2),
                         np.linspace(6, 8, num=N/2)])
    y = 0.075*x + norm.rvs(0, noise_std, size=N)
    x = (x - 4.0) / 4.0
    x = x.reshape((N, 1))
    return {'x': x, 'y': y}

Example #5

def build_toy_dataset(N=40, noise_std=0.1):
    ed.set_seed(0)
    x  = np.concatenate([np.linspace(0, 2, num=N/2),
                         np.linspace(6, 8, num=N/2)])
    y = 0.075*x + norm.rvs(0, noise_std, size=N)
    x = (x - 4.0) / 4.0
    x = x.reshape((N, 1))
    return {'x': x, 'y': y}

Example #6

File: variationals.py Project: MLDL/edward

 def sample_noise(self, size):
     """
     eps = sample_noise() ~ s(eps)
     s.t. z = reparam(eps; lambda) ~ q(z | lambda)
     """
     # Not using this, since TensorFlow has a large overhead
     # whenever calling sess.run().
     #samples = sess.run(tf.random_normal(self.samples.get_shape()))
     return norm.rvs(size=(size, self.num_vars))

Example #7

def build_toy_dataset(N=40, noise_std=0.1):
    D = 1
    x = np.concatenate(
        [np.linspace(0, 2, num=N / 2),
         np.linspace(6, 8, num=N / 2)])
    y = np.cos(x) + norm.rvs(0, noise_std, size=N)
    x = (x - 4.0) / 4.0
    x = x.reshape((N, D))
    return {'x': x, 'y': y}

Example #8

File: tf_hierarchical_logistic_regression.py Project: neverspill/edward

def build_toy_dataset(N, noise_std=0.1):
    D = 1
    x = np.linspace(-3, 3, num=N)
    y = np.tanh(x) + norm.rvs(0, noise_std, size=N)
    y[y < 0.5] = 0
    y[y >= 0.5] = 1
    x = (x - 4.0) / 4.0
    x = x.reshape((N, D))
    return x, y

Example #9

File: variationals.py Project: fangzheng354/edward

 def sample_noise(self, size):
     """
     eps = sample_noise() ~ s(eps)
     s.t. z = reparam(eps; lambda) ~ q(z | lambda)
     """
     # Not using this, since TensorFlow has a large overhead
     # whenever calling sess.run().
     #samples = sess.run(tf.random_normal(self.samples.get_shape()))
     return norm.rvs(size=size)

Example #10

File: bayesian_linear_regression_test.py Project: bradleyhb/edward

def build_toy_dataset(n_data=40, coeff=np.random.randn(10), noise_std=0.1):
    n_dim = len(coeff)
    x = np.random.randn(n_data, n_dim)
    y = np.dot(x, coeff) + norm.rvs(0, noise_std, size=n_data).reshape((n_data,))
    y = y.reshape((n_data, 1))

    data = np.concatenate((y, x), axis=1)
    data = tf.constant(data, dtype=tf.float32)
    return ed.Data(data)

Example #11

File: bayesian_nn_analytic_kl.py Project: appcoreopc/edward

def build_toy_dataset(n_data=40, noise_std=0.1):
    ed.set_seed(0)
    D = 1
    x  = np.concatenate([np.linspace(0, 2, num=n_data/2),
                         np.linspace(6, 8, num=n_data/2)])
    y = np.cos(x) + norm.rvs(0, noise_std, size=n_data)
    x = (x - 4.0) / 4.0
    x = x.reshape((n_data, D))
    return {'x': x, 'y': y}

Example #12

File: hierarchical_logistic_regression.py Project: sgmqs/edward

def build_toy_dataset(N=40, noise_std=0.1):
    ed.set_seed(0)
    D = 1
    x = np.linspace(-3, 3, num=N)
    y = np.tanh(x) + norm.rvs(0, noise_std, size=N)
    y[y < 0.5] = 0
    y[y >= 0.5] = 1
    x = (x - 4.0) / 4.0
    x = x.reshape((N, D))
    return {'x': x, 'y': y}

Example #13

File: hierarchical_logistic_regression.py Project: appcoreopc/edward

def build_toy_dataset(n_data=40, noise_std=0.1):
    ed.set_seed(0)
    D = 1
    x  = np.linspace(-3, 3, num=n_data)
    y = np.tanh(x) + norm.rvs(0, noise_std, size=n_data)
    y[y < 0.5] = 0
    y[y >= 0.5] = 1
    x = (x - 4.0) / 4.0
    x = x.reshape((n_data, D))
    return {'x': x, 'y': y}

Example #14

File: bayesian_linear_regression_plot.py Project: aporia3517/edward

def build_toy_dataset(n_data=40, noise_std=0.1):
    ed.set_seed(0)
    x  = np.concatenate([np.linspace(0, 2, num=n_data/2),
                         np.linspace(6, 8, num=n_data/2)])
    y = 0.075*x + norm.rvs(0, noise_std, size=n_data)
    x = (x - 4.0) / 4.0
    x = x.reshape((n_data, 1))
    y = y.reshape((n_data, 1))
    data = np.concatenate((y, x), axis=1) # n_data x 2
    data = tf.constant(data, dtype=tf.float32)
    return ed.Data(data)

Example #15

File: bayesian_linear_regression.py Project: yluo42/edward

def build_toy_dataset(n_data=40, noise_std=0.1):
    ed.set_seed(0)
    x = np.concatenate(
        [np.linspace(0, 2, num=n_data / 2),
         np.linspace(6, 8, num=n_data / 2)])
    y = 0.075 * x + norm.rvs(0, noise_std, size=n_data)
    x = (x - 4.0) / 4.0
    x = x.reshape((n_data, 1))
    y = y.reshape((n_data, 1))
    data = np.concatenate((y, x), axis=1)  # n_data x 2
    data = tf.constant(data, dtype=tf.float32)
    return ed.Data(data)

Example #16

File: bayesian_linear_regression_test.py Project: 313-Ventures/edward

def build_toy_dataset(coeff, n_data=40, n_data_test=20, noise_std=0.1):
    ed.set_seed(0)
    n_dim = len(coeff)
    x = np.random.randn(n_data+n_data_test, n_dim)
    y = np.dot(x, coeff) + norm.rvs(0, noise_std, size=(n_data+n_data_test))
    y = y.reshape((n_data+n_data_test, 1))

    data = np.concatenate((y[:n_data,:], x[:n_data,:]), axis=1)
    data = tf.constant(data, dtype=tf.float32)

    data_test = np.concatenate((y[n_data:,:], x[n_data:,:]), axis=1)
    data_test = tf.constant(data_test, dtype=tf.float32)
    return ed.Data(data), ed.Data(data_test)

Example #17

File: hierarchical_logistic_regression.py Project: ShuaiW/edward

def build_toy_dataset(n_data=40, noise_std=0.1):
    ed.set_seed(0)
    D = 1
    x  = np.linspace(-3, 3, num=n_data)
    y = np.tanh(x) + norm.rvs(0, noise_std, size=n_data)
    y[y < 0.5] = 0
    y[y >= 0.5] = 1
    x = (x - 4.0) / 4.0
    x = x.reshape((n_data, D))
    y = y.reshape((n_data, 1))
    data = np.concatenate((y, x), axis=1) # n_data x (D+1)
    data = tf.constant(data, dtype=tf.float32)
    return ed.Data(data)

Example #18

def build_toy_dataset(n_data=40, noise_std=0.1):
    ed.set_seed(0)
    D = 1
    x = np.linspace(-3, 3, num=n_data)
    y = np.tanh(x) + norm.rvs(0, noise_std, size=n_data)
    y[y < 0.5] = 0
    y[y >= 0.5] = 1
    x = (x - 4.0) / 4.0
    x = x.reshape((n_data, D))
    y = y.reshape((n_data, 1))
    data = np.concatenate((y, x), axis=1)  # n_data x (D+1)
    data = tf.constant(data, dtype=tf.float32)
    return ed.Data(data)

Example #19

File: bayesian_linear_regression_test.py Project: dcjones/edward

def build_toy_dataset(coeff, n_data=40, n_data_test=20, noise_std=0.1):
    ed.set_seed(0)
    n_dim = len(coeff)
    x = np.random.randn(n_data + n_data_test, n_dim)
    y = np.dot(x, coeff) + norm.rvs(0, noise_std, size=(n_data + n_data_test))
    y = y.reshape((n_data + n_data_test, 1))

    data = np.concatenate((y[:n_data, :], x[:n_data, :]), axis=1)
    data = tf.constant(data, dtype=tf.float32)

    data_test = np.concatenate((y[n_data:, :], x[n_data:, :]), axis=1)
    data_test = tf.constant(data_test, dtype=tf.float32)
    return ed.Data(data), ed.Data(data_test)

Example #20

File: test_norm_rvs.py Project: bradleyhb/edward

def _test(loc, scale, size):
    val_est = norm.rvs(loc, scale, size=size).shape
    val_true = (size, ) + np.asarray(loc).shape
    assert val_est == val_true

Example #21

def build_toy_dataset(N, coeff=np.random.randn(10), noise_std=0.1):
    n_dim = len(coeff)
    x = np.random.randn(N, n_dim).astype(np.float32)
    y = np.dot(x, coeff) + norm.rvs(0, noise_std, size=N)
    return x, y

Example #22

File: test_norm_rvs.py Project: LiuFang816/SALSTM_py_data

 def _test(self, loc, scale, size):
     val_est = norm.rvs(loc, scale, size=size).shape
     val_true = (size, ) + np.asarray(loc).shape
     assert val_est == val_true

Example #23

File: getting_started_example.py Project: TalkingData/edward

def build_toy_dataset(N=50, noise_std=0.1):
    x = np.linspace(-3, 3, num=N)
    y = np.cos(x) + norm.rvs(0, noise_std, size=N)
    x = x.reshape((N, 1))
    return {'x': x, 'y': y}

Example #24

def build_toy_dataset(N=50, noise_std=0.1):
    x = np.linspace(-3, 3, num=N)
    y = np.cos(x) + norm.rvs(0, noise_std, size=N)
    x = x.reshape((N, 1))
    return x, y

Example #25

File: bayesian_linear_regression_test.py Project: appcoreopc/edward

def build_toy_dataset(n_data=40, coeff=np.random.randn(10), noise_std=0.1):
    n_dim = len(coeff)
    x = np.random.randn(n_data, n_dim).astype(np.float32)
    y = np.dot(x, coeff) + norm.rvs(0, noise_std, size=n_data)
    return {'x': x, 'y': y}