本文整理汇总了Python中Matrix.Matrix.getMatrix方法的典型用法代码示例。如果您正苦于以下问题：Python Matrix.getMatrix方法的具体用法？Python Matrix.getMatrix怎么用？Python Matrix.getMatrix使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Matrix.Matrix的用法示例。
在下文中一共展示了Matrix.getMatrix方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: range
# 需要导入模块: from Matrix import Matrix [as 别名]
# 或者: from Matrix.Matrix import getMatrix [as 别名]
enhancer = ImageEnhance.Contrast(im)
im = enhancer.enhance(2)
#
#im = im.convert('P')
#
#im.show()
#print im.format, im.mode, im.size
#Lim = im
Lim = im.convert('L')
#Lim.save('E:/Downloads/captchaxx.jpg')
threshold = 80
table = []
for i in range(256):
if i < threshold:
table.append(0)
else:
table.append(1)
bim = Lim.point(table, '1')
m=Matrix(bim)
h=MatrixHandler(m.getMatrix())
h.doHandler()
#bim.show()
##
#Lim.save('captchaxxx.jpg')
# 示例2: LinearRegression
# 需要导入模块: from Matrix import Matrix [as 别名]
# 或者: from Matrix.Matrix import getMatrix [as 别名]
class LinearRegression(object):
def __init__(self, data=None, degree=1):
""" initialize object
@param data: path to data file
@param degree: degree of polynomial to solve (e.g., quadratic)
"""
self.independent_variables = Matrix()
self.dependent_variables = Matrix()
self.coefficients = list()
# make sure degree is at least 1
if degree < 1:
degree = 1
# if data is specified, load it and set independent and dependent variables accordingly
if data is not None:
document = Document().open(filePath=data, splitLines=True, splitTabs=True)
append_to_independent_variables = self.independent_variables.append
append_to_dependent_variables = self.dependent_variables.append
# loop through the rows in the document to get data
for row in document:
new_row = [float(value) for value in row]
dependent_variable_row = [new_row[-1]]
independent_variable_row = [new_row[0] ** i for i in xrange(degree + 1)]
# print independent_variable_row, new_row, dependent_variable_row
# append_to_independent_variables(new_row[:-1])
append_to_independent_variables(independent_variable_row)
# append_to_dependent_variables(new_row[-1:])
append_to_dependent_variables(dependent_variable_row)
# print self.independent_variables.matrix
self.coefficients = self.getCoefficients([self.independent_variables, self.dependent_variables])
def getCoefficients(self, data=None):
""" solve for the coefficient weights from the data
@param data: list of independent and dependent variables
@return list of coefficients
"""
# if data is specified, set X and Y matrices to it
if data is not None:
X = data[0].getMatrix(True)
Y = data[1].getMatrix(True)
Xt = data[0].getMatrix(True)
Xt.transpose()
# set X and Y matrices to object's data if none specified
else:
X = self.independent_variables.getMatrix(True)
Y = self.dependent_variables.getMatrix(True)
Xt = self.independent_variables.getMatrix(True)
Xt.transpose()
XtX = (Xt * X).getInverse()
XtY = Xt * Y
coefficients = XtX * XtY
coefficients.transpose()
return coefficients[0]
def getResiduals(self):
residuals = list()
append = residuals.append
for i in xrange(len(self.independent_variables)):
y = self.dependent_variables[i][0]
y_estimated = 0
for j in xrange(len(self.coefficients)):
y_estimated += self.coefficients[j] * self.independent_variables[i][j]
append(y - y_estimated)
return residuals
def getRSquared(self):
mean = self.getMean(self.dependent_variables)
residuals = self.getResiduals()
residual_variation = [r ** 2 for r in residuals]
total_variation = [(y[0] - mean) ** 2 for y in self.dependent_variables]
return 1 - (sum(residual_variation) / sum(total_variation))
def getCorrelationCoefficient(self):
return math.sqrt(self.getRSquared())
def getMean(self, data=None, predictor=0):
if data is None:
data = self.independent_variables
#.........这里部分代码省略.........