def returnCheckerBoardFunction(self, Ndata: int, nx: int, ny: int, delta: float):
"""initCheckerBoardFunction
Generates two classes similar to a chekerBoard table. One classe would be in the same position
as the black part of the board and the second in the white one
Parameters:
INPUT:
Ndata: number of instance of each quadrant
nx: number of quadrants in x
ny: number of quadrants in y
delta: scramble factor, a lower delta means more entangled classes
OUTPUT:
cls1, cls2 : objects from classData with encapsulates all data from a given sub-class.
Example:
import LoadData
import matplotlib.pyplot as plt
myData = LoadData.clsWorkingData()
cls1, cls2 = myData.initCheckerBoardFunction(50,2,2,0.5)
plt.plot(cls1.data[:,0],cls1.data[:,1],'g*')
plt.plot(cls2.data[:,0],cls2.data[:,1],'rd'),
plt.show()
Modified:
(LEO) : 17/01/2016
Version:
v0.1
"""
cls1 = DataModel.clsData(1, 1, 0)
cls2 = DataModel.clsData(0, 1, 0)
data1 = []
data2 = []
i1 = 0
i2 = 0
for k in range(0, Ndata):
for i in range(0, nx):
for j in range(0, ny):
if divmod(i + j, 2)[1] == 1:
dx = -delta + 2.0 * delta * random.random()
dy = -delta + 2 * delta * random.random()
data1.append([i + dx, j + dy])
i1 += 1
else:
dx = -delta + 2.0 * delta * random.random()
dy = -delta + 2.0 * delta * random.random()
data2.append([i + dx, j + dy])
i2 += 1
data1 = np.matrix(data1)
data2 = np.matrix(data2)
cls1.setData(data1)
cls2.setData(data2)
return cls1, cls2
def returnBananaDataset(self, N: (int, int), p: (float, float), r: float, s: float):
"""initCheckerBoardFunction
Generates two classes similar to two bananas - semi-circle classes. One classe is mirrowed and shifted related to another.
Parameters:
INPUT:
N: (int,int) tuple, indicating the number of instances of each class
p: (float,float) a tuple responsible for shifting the center of each class
r: the ray of the "bananas"
s: scramble factor, a higher s means more entangled classes
OUTPUT:
cls1, cls2 : objects from classData with encapsulates all data from a given sub-class.
Example:
import LoadData
import matplotlib.pyplot as plt
myData = LoadData.clsWorkingData()
cls1, cls2 = myData.returnBananaDataset((200,200), (1.0,1.0), 1.7, 0.2)
plt.plot(cls1.data[:,0],cls1.data[:,1],'g*')
plt.plot(cls2.data[:,0],cls2.data[:,1],'rd'),
plt.show()
Modified:
(LEO) : 17/01/2016
Version:
v0.1
"""
cls1 = DataModel.clsData(1, 1, 0)
cls2 = DataModel.clsData(0, 1, 0)
domaina = np.array(0.125 * np.pi + np.random.rand(N[0], 1) * 1.25 * np.pi)
data1 = np.matrix(np.column_stack((r * np.sin(domaina), r * np.cos(domaina)))) + np.matrix(
np.random.rand(N[0], 2)
)
cls1.setData(data1)
domainb = np.array(0.375 * np.pi - np.random.rand(N[0], 1) * 1.25 * np.pi)
data2 = np.matrix(np.column_stack((r * np.sin(domainb) - p[0], r * np.cos(domainb) - p[1]))) + np.matrix(
np.random.rand(N[0], 2)
)
cls2.setData(data2)
return cls1, cls2
def returnLineSegmentDataset(self, p: (float, float), r: float):
"""initCheckerBoardFunction
v0.1
"""
cls1 = DataModel.clsData(1, 1, 0)
cls2 = DataModel.clsData(0, 1, 0)
x = np.array(np.arange(p[0], p[1], r)).transpose()
n = x.shape[0]
y = np.array(x) + np.random.rand(1, n) * r
data = np.vstack((x, y))
cls1.setData(data.transpose())
return cls1
def __init__(self, set_id = 0, in_collision = 0, breaks = 0, data = [], obj_data = DataModel.clsData()): if len(obj_data.data)==0: DataModel.clsData.__init__(self,set_id,in_collision,breaks,data) else: DataModel.clsData.__init__(self,obj_data.set_id,obj_data.in_collision,obj_data.breaks,obj_data.data) self.projections = Projections.clsProjectionOverAxis(self.data,self.word_ref_center,self.pi)
def returnPointsDataset(self):
"""initCheckerBoardFunction
v0.1
"""
cls1 = DataModel.clsData(1, 1, 0)
cls2 = DataModel.clsData(0, 1, 0)
px = [-2.0, -2.0, 2.0, 2.0]
py = [-2.0, 2.0, -2.0, 2.0]
x = np.array(px).transpose()
n = x.shape[0]
y = np.array(py).transpose()
data = np.vstack((x, y))
cls1.setData(data.transpose())
return cls1, cls1
def breakThisBondingBoxUsingKDE(self): idx_max_lambda = np.argmax(self.Lambda) c_axis = np.array(self.projections.projection_over_pi[idx_max_lambda]) i_break = self.kernelDensityEstimation() new_data_1 = [] new_data_2 = [] for i in range(0,self.dataLen): if c_axis[i] < i_break: new_data_1.append(self.data[i,:]) else: new_data_2.append(self.data[i,:]) pcls1 = DataModel.clsData(self.set_id,1,self.breaks) pcls2 = DataModel.clsData(self.set_id,1,self.breaks) pcls1.setData(new_data_1) pcls2.setData(new_data_2) new_obb_1 = clsOBHB(obj_data = pcls1) new_obb_2 = clsOBHB(obj_data = pcls2) return new_obb_1,new_obb_2