def map_replace(str_: str,
key: [list, str] = None,
value: [list, str] = None,
maps: dict = None) -> str:
"""映射替换
:param str_: 字符串
:param key: 替换字符
:param value: 被替换的字符
:param maps:字符映射
:return: 替换完毕的字符串
"""
if maps:
key, value = '', ''
for k, v in maps.items():
key += k
value += v
else:
if len(key) != len(value):
raise CoordinateLengthNotEqualError('替换的长度必须相同！')
if isinstance(key, list):
key = ''.join(key)
if isinstance(value, list):
value = ''.join(value)
m = str.maketrans(key, value)
return str_.translate(m)
def mahalanobis_distance(coordinate_x, coordinate_y):
"""
求解马氏距离
:param coordinate_x: x轴向量
:param coordinate_y: y轴向量
:return result: 两个点的马氏距离
"""
if len(coordinate_x) != len(coordinate_y):
raise CoordinateLengthNotEqualError("坐标长度不一致")
# 马氏距离要求样本数要大于维数，否则无法求协方差矩阵
# 此处进行转置，表示10个样本，每个样本2维
x = np.vstack([coordinate_x, coordinate_y])
x_t = x.T # 转置
s = np.cov(x) # 两个维度之间协方差矩阵
s_i = np.linalg.inv(s) # 协方差矩阵的逆矩阵
# 马氏距离计算两个样本之间的距离，此处共有4个样本，两两组合，共有6个距离。
n = x_t.shape[0]
result = []
for i in range(0, n):
for j in range(i + 1, n):
delta = x_t[i] - x_t[j]
point = x_t[i], x_t[j]
distance = np.sqrt(np.dot(np.dot(delta, s_i), delta.T))
result.append((point, distance))
return result
def map_replace(str_: str, key: [list, str] = None, value: [list, str] = None, maps: dict = None) -> str:
"""映射替换,最好使用maps字典映射
>>> print(map_replace('[中国]', '[]', '【】')) #【中国】
>>> print(map_replace('[中国]', maps={'[': '【', ']': '】'})) #【中国】
:param str_: 字符串
:param key: 替换字符
:param value: 被替换的字符
:param maps: 字符映射
:return: 替换完毕的字符串
"""
if maps:
strings = str_
for k, v in maps.items():
strings = strings.replace(k, v)
return strings
else:
if len(key) != len(value):
raise CoordinateLengthNotEqualError('替换的长度必须相同！')
if isinstance(key, list):
key = ''.join(key)
if isinstance(value, list):
value = ''.join(value)
m = str.maketrans(key, value)
return str_.translate(m)
def manhattan_distance(coordinate_p, coordinate_q):
"""
曼哈顿距离
:param coordinate_p: p坐标
:param coordinate_q: q坐标
:return: 曼哈顿距离值
"""
if len(coordinate_q) != len(coordinate_p):
raise CoordinateLengthNotEqualError("坐标长度不一致")
point = (abs(x - y) for x, y in zip(coordinate_q, coordinate_p))
return sum(point)
def chebyshev_distance(coordinate_p, coordinate_q):
"""
传入的是两个可迭代对象,每一个是一个n位坐标,比如:p=(x1,x2,x3....xn),q=(y1,y2,y3....yn)
:param coordinate_p: p坐标
:param coordinate_q: q坐标
:return: 切比雪夫距离(棋盘距离)
"""
if len(coordinate_q) != len(coordinate_p):
raise CoordinateLengthNotEqualError("坐标长度不一致")
coordinate = []
for x, y in zip(coordinate_p, coordinate_q):
coordinate.append(abs(x - y))
return max(coordinate)
def euclidean_distance(coordinate_p, coordinate_q):
"""
欧氏距离
:param coordinate_p: p坐标
:param coordinate_q: q坐标
:return: 欧氏距离值
"""
if len(coordinate_q) != len(coordinate_p):
raise CoordinateLengthNotEqualError("坐标长度不一致")
numerator = 0
for x, y in zip(coordinate_p, coordinate_q):
numerator += (x - y)**2
return math.sqrt(numerator)
def cosine_distance(coordinate_p, coordinate_q):
"""
余弦距离又叫余弦角度
:param coordinate_p:p坐标
:param coordinate_q:q坐标
:return:余弦距离
"""
if len(coordinate_q) != len(coordinate_p):
raise CoordinateLengthNotEqualError("坐标长度不一致")
numerator, denominator = 0, 1 # 分子和父母
for x, y in zip(coordinate_p, coordinate_q):
numerator += x * y
q = [x**2 for x in coordinate_q]
p = [y**2 for y in coordinate_p]
return numerator / math.sqrt(sum(q) * sum(p))
def wraps(x, y):
if not (x and y):
raise ParameterNotEmptyError("参数不能为空")
if len(x) != len(y):
raise CoordinateLengthNotEqualError("两个参数长度不一致")
return fun(x, y)