class Sample(ProbabilitySpace):
figsize = (5.5, 5.5)
alpha = 0.3
ptsize = 5
sample_ptsize = 60
def __init__(self, nblue, nred, ndraw, draw_fig=True):
self.nblue, self.nred, self.ndraw = nblue, nred, ndraw
self.box = BoxModel(["B"] * nblue + ["R"] * nred, replace=False)
self.X = np.random.sample((nblue + nred, 2))
self.draw_fig = draw_fig
if draw_fig:
self.draw()
def draw(self, color={"R": "red", "B": "blue"}):
self.figure.clf()
ax, X = self.axes, self.X
ax.scatter(X[: self.nblue, 0], X[: self.nblue, 1], s=self.ptsize, color=color["B"], alpha=self.alpha)
ax.scatter(X[self.nblue :, 0], X[self.nblue :, 1], s=self.ptsize, color=color["R"], alpha=self.alpha)
ax.set_xticks([])
ax.set_xlim([-0.01, 1.01])
ax.set_yticks([])
ax.set_ylim([-0.01, 1.01])
@property
def figure(self):
if not hasattr(self, "_figure"):
self._figure = plt.figure(figsize=self.figsize)
self._axes = self._figure.gca()
return self._figure
@property
def axes(self):
self.figure
return self._axes
def draw_sample_pts(self, bgcolor={"R": "red", "B": "blue"}, color={"R": "red", "B": "blue"}):
self.draw(color=bgcolor)
ax, X = self.axes, self.X
sample = self.box.sample(self.ndraw)
nblue = np.sum([s == "B" for s in sample])
nred = self.ndraw - nblue
ax.scatter(
X[:nblue, 0], X[:nblue, 1], s=self.sample_ptsize, color=color["B"], alpha=0.8, edgecolor="gray", linewidth=2
)
ax.scatter(
X[-nred:, 0], X[-nred:, 1], s=self.sample_ptsize, color=color["R"], alpha=0.8, edgecolor="gray", linewidth=2
)
return self.figure
def trial(self, bgcolor={"R": "red", "B": "blue"}, color={"R": "red", "B": "blue"}):
sample = self.box.sample(self.ndraw)
if self.draw_fig:
self.draw_sample_pts(color=color, bgcolor=bgcolor)
self.outcome = np.mean([s == "B" for s in sample])
return self.outcome
class Sample(ProbabilitySpace):
figsize = (5.5, 5.5)
alpha = 0.3
ptsize = 5
sample_ptsize = 60
def __init__(self, nblue, nred, ndraw, draw_fig=True):
self.nblue, self.nred, self.ndraw = nblue, nred, ndraw
self.box = BoxModel(['B']*nblue + ['R']*nred, replace=False)
self.X = np.random.sample((nblue+nred, 2))
self.draw_fig = draw_fig
if draw_fig:
self.draw()
def draw(self, color={'R':'red','B':'blue'}):
self.figure.clf()
ax, X = self.axes, self.X
ax.scatter(X[:self.nblue,0], X[:self.nblue,1], s=self.ptsize, color=color['B'], alpha=self.alpha)
ax.scatter(X[self.nblue:,0], X[self.nblue:,1], s=self.ptsize, color=color['R'], alpha=self.alpha)
ax.set_xticks([]); ax.set_xlim([-0.01,1.01])
ax.set_yticks([]); ax.set_ylim([-0.01,1.01])
@property
def figure(self):
if not hasattr(self, "_figure"):
self._figure = plt.figure(figsize=self.figsize)
self._axes = self._figure.gca()
return self._figure
@property
def axes(self):
self.figure
return self._axes
def draw_sample_pts(self, bgcolor={'R':'red','B':'blue'},
color={'R':'red','B':'blue'}):
self.draw(color=bgcolor)
ax, X = self.axes, self.X
sample = self.box.sample(self.ndraw)
nblue = np.sum([s == 'B' for s in sample])
nred = self.ndraw - nblue
ax.scatter(X[:nblue,0], X[:nblue,1], s=self.sample_ptsize, color=color['B'], alpha=.8, edgecolor='gray', linewidth=2)
ax.scatter(X[-nred:,0], X[-nred:,1], s=self.sample_ptsize, color=color['R'], alpha=.8, edgecolor='gray', linewidth=2)
return self.figure
def trial(self, bgcolor={'R':'red','B':'blue'},
color={'R':'red','B':'blue'}):
sample = self.box.sample(self.ndraw)
if self.draw_fig:
self.draw_sample_pts(color=color, bgcolor=bgcolor)
self.outcome = np.mean([s == 'B' for s in sample])
return self.outcome
class BloodPressure(ProbabilitySpace):
figsize = (5.5, 5.5)
alpha = 0.1
ptsize = 5
sample_ptsize = 60
def __init__(self, draw_fig=True):
self.npop, self.ndraw = 5000, 50
self.box = BoxModel(np.random.random_integers(-15, 0,
size=(self.npop,)),
replace=True)
self.X = (np.mgrid[0:1:10j,0:1:5j].reshape((2,50)) +
np.random.sample((2,50)) * 0.05)
self.BG = np.random.sample((self.npop,2))
self.X = self.X.T
self.draw_fig = draw_fig
if draw_fig:
self.draw()
def draw(self, color={'R':'red','B':'blue'}):
self.figure.clf()
ax, X, BG = self.axes, self.X, self.BG
ax.scatter(BG[:,0], BG[:,1], s=self.ptsize, color='gray', alpha=self.alpha)
ax.set_xticks([]); ax.set_xlim([-0.1,1.1])
ax.set_yticks([]); ax.set_ylim([-0.1,1.1])
@property
def figure(self):
if not hasattr(self, "_figure"):
self._figure = plt.figure(figsize=self.figsize)
self._axes = self._figure.gca()
return self._figure
@property
def axes(self):
self.figure
return self._axes
def draw_sample_pts(self, bgcolor={'R':'red','B':'blue'},
color={'R':'red','B':'blue'}):
self.draw(color=bgcolor)
ax, X, sample = self.axes, self.X, self._sample
mean, sd = self.outcome
for i in range(50):
ax.text(X[i,0], X[i,1], '%d' % sample[i], color='red')
ax.set_title("average(sample)=%0.1f, SD(sample)=%0.1f" % (np.mean(sample), np.std(sample)), fontsize=15)
return self.figure
def trial(self, bgcolor={'R':'red','B':'blue'},
color={'R':'red','B':'blue'}):
self._sample = self.box.sample(self.ndraw)
self.outcome = np.mean(self._sample), np.std(self._sample)
if self.draw_fig:
self.draw_sample_pts(color=color, bgcolor=bgcolor)
return self.outcome
class BloodPressure(ProbabilitySpace):
figsize = (5.5, 5.5)
alpha = 0.1
ptsize = 5
sample_ptsize = 60
def __init__(self, draw_fig=True):
self.npop, self.ndraw = 5000, 50
self.box = BoxModel(np.random.random_integers(-15,
0,
size=(self.npop, )),
replace=True)
self.X = (np.mgrid[0:1:10j, 0:1:5j].reshape(
(2, 50)) + np.random.sample((2, 50)) * 0.05)
self.BG = np.random.sample((self.npop, 2))
self.X = self.X.T
self.draw_fig = draw_fig
if draw_fig:
self.draw()
def draw(self, color={'R': 'red', 'B': 'blue'}):
self.figure.clf()
ax, X, BG = self.axes, self.X, self.BG
ax.scatter(BG[:, 0],
BG[:, 1],
s=self.ptsize,
color='gray',
alpha=self.alpha)
ax.set_xticks([])
ax.set_xlim([-0.1, 1.1])
ax.set_yticks([])
ax.set_ylim([-0.1, 1.1])
@property
def figure(self):
if not hasattr(self, "_figure"):
self._figure = plt.figure(figsize=self.figsize)
self._axes = self._figure.gca()
return self._figure
@property
def axes(self):
self.figure
return self._axes
def draw_sample_pts(self,
bgcolor={
'R': 'red',
'B': 'blue'
},
color={
'R': 'red',
'B': 'blue'
}):
self.draw(color=bgcolor)
ax, X, sample = self.axes, self.X, self._sample
mean, sd = self.outcome
for i in range(50):
ax.text(X[i, 0], X[i, 1], '%d' % sample[i], color='red')
ax.set_title("average(sample)=%0.1f, SD(sample)=%0.1f" %
(np.mean(sample), np.std(sample)),
fontsize=15)
return self.figure
def trial(self,
bgcolor={
'R': 'red',
'B': 'blue'
},
color={
'R': 'red',
'B': 'blue'
}):
self._sample = self.box.sample(self.ndraw)
self.outcome = np.mean(self._sample), np.std(self._sample)
if self.draw_fig:
self.draw_sample_pts(color=color, bgcolor=bgcolor)
return self.outcome