jasonweiyi

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from torch.utils.tensorboard import SummaryWriter
import numpy as np

def vis_confusion(writer, step):
    """
    Visualization of confusion matrix

    Parameters:

jasonweiyi

import statsmodels.api as sm

data = sm.datasets.get_rdataset("Duncan", "carData")
X = data.data[['prestige', 'education']]
X = sm.add_constant(X)
Y = data.data['income']

model = sm.OLS(Y, X)
results = model.fit()
print(results.summary())

jasonweiyi

# This code is adapted from the official GPflow document page:
# https://gpflow.readthedocs.io/en/master/notebooks/advanced/gps_for_big_data.html
import numpy as np
import gpflow
import tensorflow as tf
import matplotlib.pyplot as plt
from gpflow.ci_utils import ci_niter

plt.style.use("ggplot")

jasonweiyi

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm



n = 2

jasonweiyi

import seaborn as sns
import numpy as np
import tensorflow as tf
from gpflow.models import VGP
from matplotlib.ticker import MaxNLocator
import gpflow
from gpflow import default_float
import matplotlib.pyplot as plt

jasonweiyi

from generate_training_data import generate_points
from gp import ExponentialSquaredKernel, GP
import numpy as np
import matplotlib.pyplot as plt

lengthscale = 1.
signal_variance = 1.
noise_variance = 0.1

X, Y = generate_points(start=np.pi * 0, end=np.pi*2)

jasonweiyi

import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import math

mu = 0
variance = 1
sigma = math.sqrt(variance)
x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)
fig = plt.figure()

jasonweiyi

import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow_probability import sts

tf.reset_default_graph()
prediction_steps = 12
# skyline is an array storing mountain skyline data points.
training_data = skyline[:-prediction_steps]

# Build local linear trend model.

jasonweiyi

import matplotlib.pyplot as plt
import scipy.stats
import numpy as np
from matplotlib.ticker import NullFormatter

x_min = -20.0
x_max = 20.0

x = np.linspace(x_min, x_max, 100)

jasonweiyi

import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow_probability import sts
import seaborn as sns
from matplotlib import pylab as plt
from matplotlib.ticker import NullFormatter


def plot_forecast(x, y, forecast_mean, forecast_scale, title, x_locator=None, x_formatter=None):