def _make(self, fig):
sh = DATA.getFigure(fig)
for i in range(4):
peice = sh[i]
self.shape.append([peice[0], peice[1]])
self.position.append([peice[0] + 1, peice[1] + 1])
self.color = DATA.getColor(fig)
def paintEvent(self, e):
qp = QPainter()
qp.begin(self)
qp.setPen(QColor(200, 100, 100))
for i in range(200):
x = i % 10
y = (i - x) // 10
if self.board[i] != 0:
tpl = DATA.getColor(self.board[i])
qp.setBrush(QColor(tpl[0], tpl[1], tpl[2]))
qp.drawRect(x * self.cube, y * self.cube, self.cube, self.cube)
qp.setBrush(
QColor(self.figure.color[0], self.figure.color[1],
self.figure.color[2]))
for i in range(4):
qp.drawRect(self.figure.position[i][0] * self.cube,
self.figure.position[i][1] * self.cube, self.cube,
self.cube)
qp.drawLine(self.width() - 1, 0, self.width() - 1, self.height())
qp.drawLine(0, self.height() - 1, self.width(), self.height() - 1)
qp.end()
import matplotlib.pyplot as plt
import numpy as np
from Data import DATA
from Sketching_methods import gaussian_projection
from Sketching_methods import hadamard_projection
# Parameters
c = np.linspace(0.1, 1, 20)
rep = 50
track_msd = np.zeros((20, 2))
track_flight = np.zeros((20, 2))
# MSD
n = 2000
np.random.seed(13)
data = DATA(type='MSD', n=n, p=90)
i = 0
for xi in c:
print(i)
r = int(n * xi)
for k in range(rep):
track_msd[i, 0] = track_msd[i, 0] + gaussian_projection(data, r)[2]
track_msd[i, 1] = track_msd[i, 1] + hadamard_projection(data, r)[2]
i = i + 1
track_msd = track_msd / rep
# nycflight
np.random.seed(130)
data = DATA(type='nycflight', n=2000, p=21)
i = 0
for xi in c:
gamma = p / n
np.random.seed(130)
X = np.random.randn(n, p)
beta = np.random.rand(p, 1)
rep = 50
c = np.linspace(0.1, 1, 20)
track = np.empty((20, 4))
# uniform sampling
i = 0
for xi in c:
print(i)
r = int(n * xi)
vpro = np.empty((rep, 4))
for k in range(rep):
data = DATA(type='Gaussian', n=n, p=p, X=X, beta=beta)
vpro[k, :] = uniform_sampling(data, r)
track[i, :] = np.mean(vpro, axis=0)
i = i + 1
# Figure 7
d = np.linspace(0.15, 1, 500)
plt.figure(0, figsize=(10, 8))
p11 = plt.subplot(221)
p11.cla()
p11.scatter(c[1:], track[1:, 0], label='Simulation')
p11.plot(d, (1-gamma)/(d-gamma), label=r'Theory: $\frac{n-p}{r-p}$')
p11.grid(linestyle='dotted')
p11.set_ylabel('VE', fontsize=13)
p11.legend()
def dropFigure(self):
color = DATA.getColors().index(self.figure.color)
for i in range(4):
coord = self.figure.position[i][0] + self.figure.position[i][1] * 10
self.board[coord] = color