def test_empty(self):
a = np.array([[[]]])
b = np.array([[], []])
c = tile(b, 2).shape
d = tile(a, (3, 2, 5)).shape
assert_equal(c, (2, 0))
assert_equal(d, (3, 2, 0))
def __init__(self, L, N):
self.deltaX = float(L / float(N))
self.N = int(N + 1)
self.M = self.N
self.L = float(L)
self.X = []
# TODO !!! move this part to __call__ method
self.A = array(zeros([self.M, self.M]))
#self.filtera = array(self.A +1, dtype=bool)
self.filtera = self.A == self.A # COOL ! :)
self.DOF = array(tile(True, (self.M)), dtype=bool)
for i in range(self.M): # assuming DX=Constant
self.X.append(round(self.deltaX * i, 15)) # To avoid stupid 0.199
self.B = array(zeros([self.M, 1]))
self.T = array(tile(nan, (self.M, )))
# TODO for solve method
self.bcConduction = array(tile(False, (self.M, )), dtype=bool)
self.bcConvection = array(tile(False, (self.M, )), dtype=bool)
self.bcT = array(tile(True, (self.M, )), dtype=bool)
#self.bcRadiation = array(tile(False, (self.M, 1)), dtype=bool)
internalNodes = array([1., -2., 1.])
#
for i in range(1, int(self.M - 1)):
# self.A[i, range(i-1, i+2)] = internalNodes
self.B[i] *= self.deltaX**2
def test_empty(self):
a = np.array([[[]]])
b = np.array([[], []])
c = tile(b, 2).shape
d = tile(a, (3, 2, 5)).shape
assert_equal(c, (2, 0))
assert_equal(d, (3, 2, 0))
def __init__(self, L, N):
self.deltaX = float(L / float(N))
self.N = int(N + 1)
self.M = self.N
self.L = float(L)
self.X = []
# TODO !!! move this part to __call__ method
self.A = array(zeros([self.M, self.M]))
#self.filtera = array(self.A +1, dtype=bool)
self.filtera = self.A == self.A # COOL ! :)
self.DOF = array(tile(True, (self.M)), dtype=bool)
for i in range(self.M): # assuming DX=Constant
self.X.append(round(self.deltaX*i, 15)) # To avoid stupid 0.199
self.B = array(zeros([self.M, 1]))
self.T = array(tile(nan, (self.M,)))
# TODO for solve method
self.bcConduction = array(tile(False, (self.M, )), dtype=bool)
self.bcConvection = array(tile(False, (self.M, )), dtype=bool)
self.bcT = array(tile(True, (self.M, )), dtype=bool)
#self.bcRadiation = array(tile(False, (self.M, 1)), dtype=bool)
internalNodes = array([1., -2., 1.])
#
for i in range(1, int(self.M-1)):
# self.A[i, range(i-1, i+2)] = internalNodes
self.B[i] *= self.deltaX**2
def auto_toOne(matrix):
result=zeros((matrix.shape[0],matrix.shape[1]))
rows=matrix.shape[0];
coloum=matrix.shape[1];
ran=zeros((1,coloum));
ran=matrix.max(0)-matrix.min(0)
norMatrix=matrix-tile(matrix.min(0),(rows,1))
result=norMatrix/tile(ran,(rows,1))
return result,ran,matrix.min(0)
def test_kroncompare(self):
from numpy.random import randint
reps = [(2,), (1, 2), (2, 1), (2, 2), (2, 3, 2), (3, 2)]
shape = [(3,), (2, 3), (3, 4, 3), (3, 2, 3), (4, 3, 2, 4), (2, 2)]
for s in shape:
b = randint(0, 10, size=s)
for r in reps:
a = np.ones(r, b.dtype)
large = tile(b, r)
klarge = kron(a, b)
assert_equal(large, klarge)
def test_kroncompare(self):
from numpy.random import randint
reps = [(2,), (1, 2), (2, 1), (2, 2), (2, 3, 2), (3, 2)]
shape = [(3,), (2, 3), (3, 4, 3), (3, 2, 3), (4, 3, 2, 4), (2, 2)]
for s in shape:
b = randint(0, 10, size=s)
for r in reps:
a = np.ones(r, b.dtype)
large = tile(b, r)
klarge = kron(a, b)
assert_equal(large, klarge)
def test_basic(self):
a = np.array([0, 1, 2])
b = [[1, 2], [3, 4]]
assert_equal(tile(a, 2), [0, 1, 2, 0, 1, 2])
assert_equal(tile(a, (2, 2)), [[0, 1, 2, 0, 1, 2], [0, 1, 2, 0, 1, 2]])
assert_equal(tile(a, (1, 2)), [[0, 1, 2, 0, 1, 2]])
assert_equal(tile(b, 2), [[1, 2, 1, 2], [3, 4, 3, 4]])
assert_equal(tile(b, (2, 1)), [[1, 2], [3, 4], [1, 2], [3, 4]])
assert_equal(tile(b, (2, 2)), [[1, 2, 1, 2], [3, 4, 3, 4], [1, 2, 1, 2], [3, 4, 3, 4]])
def test_basic(self):
a = np.array([0, 1, 2])
b = [[1, 2], [3, 4]]
assert_equal(tile(a, 2), [0, 1, 2, 0, 1, 2])
assert_equal(tile(a, (2, 2)), [[0, 1, 2, 0, 1, 2], [0, 1, 2, 0, 1, 2]])
assert_equal(tile(a, (1, 2)), [[0, 1, 2, 0, 1, 2]])
assert_equal(tile(b, 2), [[1, 2, 1, 2], [3, 4, 3, 4]])
assert_equal(tile(b, (2, 1)), [[1, 2], [3, 4], [1, 2], [3, 4]])
assert_equal(tile(b, (2, 2)), [[1, 2, 1, 2], [3, 4, 3, 4],
[1, 2, 1, 2], [3, 4, 3, 4]])
def classify(inX,dataSet,labels,k):
dataSetSize = dataSet.shape[0]
diffMat = tile(inX,(dataSetSize,1)) -dataSet # 算距离
print(diffMat)
sqDiffMat = diffMat**2
sqDistances = sqDiffMat.sum(axis=1) #
distances = sqDistances**0.5 #
print(sqDistances)
sortedDistIndicies = distances.argsort()
classCount = {}
for i in range(k): # 距离最小的k个点
voteIlabel = labels[sortedDistIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1),reverse=True) # 排序
print(sortedClassCount)
return sortedClassCount[0][0]
def knn(group, labels, test, k):
tests = tile(test, (group.shape[0], 1))
diff = group - tests
diff_pow = diff * diff
diff_sum = sum(diff_pow, axis=1)
distance = diff_sum**0.5
argsort = distance.argsort()
print('distance:', distance)
print('argsort:', argsort)
classcount = {}
for i in arange(k):
label = labels[argsort[i]]
print('label:' + label)
classcount[label] = classcount.get(label, 0) + 1
print(classcount)
sortClasscount = sorted(classcount.items(), key=operator.itemgetter(1))
print(sortClasscount)
print('res:', sortClasscount[0][0])
def test_tile_one_repetition_on_array_gh4679(self):
a = np.arange(5)
b = tile(a, 1)
b += 2
assert_equal(a, np.arange(5))
val1[1][n] = strs[-2]
else:
res0 += np.array(line2array(strs, list_label))
val0[0][n - 8] = strs[-3]
val0[1][n - 8] = strs[-2]
n = n + 1
print("res1:")
print(res1)
print("res0:")
print(res0)
add_1 = np.hstack(((np.ones([3, 5]) * 3), np.ones([3, 1]) * 2)) #拉普拉斯正则化
add_2 = np.ones([3, 6])
diff_0 = (res0 + add_2) / (tile(res0.sum(axis=0), (3, 1)) + add_1)
diff_1 = (res1 + add_2) / (tile(res1.sum(axis=0), (3, 1)) + add_1)
print("diff_0:")
print(diff_0)
print("diff_1:")
print(diff_1)
#--------------------↑前六列训练数据----------------------
list_test = ["青绿", "蜷缩", "浊响", "清晰", "凹陷", "硬滑"]
test_1 = line2array(list_test, list_label)
print("test:")
print(test_1)
#--------------------↑前六列测试数据----------------------
p_0_pre = diff_0 * test_1
print("前六列为'否'各概率:")
print(p_0_pre)
print("前六列为'是'各概率:")
def test_empty(self):
a = np.array([[[]]])
d = tile(a, (3, 2, 5)).shape
assert_equal(d, (3, 2, 0))
# coding:utf-8 """ 格式：tile（A,reps） * A：array_like * 输入的array * reps：array_like * A沿各个维度重复的次数 """ from numpy.lib.shape_base import tile A = [1, 2] print tile(A, 2) # [1 2 1 2] """ [[1 2 1 2 1 2] [1 2 1 2 1 2]] """ # 理解 2,3这里2，3代表: 行的维度平铺2次， 列的维度 平铺3次. 最终得到 2行，6列的数组 (A是2个元素的数组* 3). print tile(A, (2, 3))
def test_tile_one_repetition_on_array_gh4679(self):
a = np.arange(5)
b = tile(a, 1)
b += 2
assert_equal(a, np.arange(5))
def getDistance(v, dataset):
rowsize, columnsize = dataset.shape
diffMat = tile(v, (rowsize, 1)) - dataset
sqDiffMat = diffMat**2
sqrtDiffMat = sqDiffMat.sum(axis=1)**0.5
return sqrtDiffMat
def test_empty(self):
a = np.array([[[]]])
d = tile(a, (3, 2, 5)).shape
assert_equal(d, (3, 2, 0))
import numpy as np
from numpy.lib.shape_base import tile
if __name__ == '__main__':
aa1 = np.array([[1, 2], [7, 8], [11, 12], [12, 12]])
print aa1
print aa1.shape
print aa1[0]
group = np.array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
print group
aa = (2, 4)
bb = tile(aa, (4, 1))
print aa1
print bb
cc = bb - aa1
print cc
dd = cc**2
print 'dd is ' + dd
ee = dd**0.5
print ee
print ee
print ee.argsort()
dd = ee.argsort()
labels = ['A', 'B', 'C', 'D']
print dd[0]
voteIlabel = labels[dd[0]]
