本文整理汇总了Python中pysgpp.DataVector.min方法的典型用法代码示例。如果您正苦于以下问题：Python DataVector.min方法的具体用法？Python DataVector.min怎么用？Python DataVector.min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pysgpp.DataVector的用法示例。
在下文中一共展示了DataVector.min方法的1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: TestDataVector
# 需要导入模块: from pysgpp import DataVector [as 别名]
# 或者: from pysgpp.DataVector import min [as 别名]
class TestDataVector(unittest.TestCase):
##
# Set up, create random DataVector and corresponding Python data structures.
# @test DataVector::get(), DataVector::set()
def setUp(self):
from pysgpp import DataVector
import random
## number of rows
self.nrows = 5
## number of columns
self.ncols = 4
## number of entries
self.N = self.nrows*self.ncols
## random list of lists
self.l_rand = [[2*(random.random()-0.5) for j in xrange(self.ncols)] for i in xrange(self.nrows)]
## same as l_rand, but flattened
self.l_rand_total = []
for li in self.l_rand:
self.l_rand_total.extend(li)
# ## Data Vector, corresponding to l_rand
# self.d_rand = DataVector(self.nrows,self.ncols)
# for i in xrange(self.N):
# self.d_rand[i] = self.l_rand_total[i]
#
# for i in xrange(self.N):
# self.assertEqual(self.d_rand[i], self.l_rand_total[i])
## Data Vector, corresponding to l_rand
self.d_rand = DataVector(self.N)
for i in xrange(self.N):
self.d_rand[i] = self.l_rand_total[i]
for i in xrange(self.N):
self.assertEqual(self.d_rand[i], self.l_rand_total[i])
##
# Constructors4.
# @test DataVector::DataVector(size_t size), DataVector::DataVector(size_t size, size_t dim), DataVector::DataVector(DataVectorDefinition &DataVectorDef), DataVector::getSize(), DataVector::getDim(), DataVector::getSize()
# @todo (pflueged) DataVector::DataVector(double *input, size_t size, size_t dim)
def testConstructor(self):
from pysgpp import DataVector
d = DataVector(2)
self.assertEqual(len(d), 2) # getSize()
# d = DataVector(2,3)
# self.assertEqual(d.getSize(), 2)
# self.assertEqual(d.getDim(), 3)
# self.assertEqual(len(d), 2*3) # getSize()
#
# d2 = DataVector(self.d_rand)
# for i in xrange(self.N):
# self.assertEqual(d2[i], self.d_rand[i])
# self.assertEqual(d2.getSize(), self.nrows)
# self.assertEqual(d2.getDim(), self.ncols)
# self.assertEqual(len(d2), self.N)
# d2[self.ncols] = -4.0
# self.assertNotEqual(d2[self.ncols], self.d_rand[self.ncols])
##
# Min, Max operations.
# @test DataVector::min(int d), DataVector::max(int d), DataVector::minmax(int d, double *min, double *max), DataVector::min(), DataVector::max()
def testMinMax(self):
# # test dimension-dependent min, max
# for j in xrange(self.ncols):
# minj = min([self.l_rand[i][j] for i in xrange(self.nrows)])
# maxj = max([self.l_rand[i][j] for i in xrange(self.nrows)])
# self.assertEqual(self.d_rand.min(j), minj)
# self.assertEqual(self.d_rand.max(j), maxj)
# mi, ma = self.d_rand.minmax(j)
# self.assertEqual(mi, minj)
# self.assertEqual(ma, maxj)
# test global min, max
self.assertEqual(self.d_rand.min(), min(self.l_rand_total))
self.assertEqual(self.d_rand.max(), max(self.l_rand_total))
##
# Operations on DataVectors.
# @test DataVector::sum(), DataVector::sqr(), DataVector::abs(), DataVector::componentwise_mult(), DataVector::componentwise_div()
def testOps(self):
from pysgpp import DataVector
# sum
self.assertAlmostEqual(self.d_rand.sum(), sum(self.l_rand_total))
# sqr
d = DataVector(self.d_rand)
d.sqr()
for i in xrange(self.N):
self.assertEqual(self.d_rand[i]**2, d[i])
# abs
d = DataVector(self.d_rand)
d.abs()
for i in xrange(self.N):
self.assertEqual(abs(self.d_rand[i]), d[i])
#.........这里部分代码省略.........