positions = [i for i in range(len(s)) if s[i] == c] 帮我分析下这段代码作用
时间: 2024-03-30 09:34:41 浏览: 9
这段代码的作用是:在字符串 s 中查找字符 c 出现的所有位置,并将这些位置存储在一个列表 positions 中。
具体来说,代码分为两个部分:
1. `range(len(s))` 生成一个长度为 s 的字符串的下标序列,即 [0,1,2,...,len(s)-1]。
2. `[i for i in range(len(s)) if s[i] == c]` 则是一个列表推导式,它会遍历上述下标序列,对每个下标 i 进行判断:如果 s[i] 等于 c,则将 i 添加到列表中。
最终,代码的输出结果是一个包含所有字符 c 在 s 中出现位置的列表 positions。
相关问题
代码如下:""" File: fromexample.py Project 12.9 Defines and tests the all pairs shortest paths algorithm of Floyd. Uses the graph from Figure 12.19 of the text, as represented in the file example.txt. """ from graph import LinkedDirectedGraph import random from arrays import Array # Functions for working with infinity def isLessWithInfinity(a, b): """Returns False if a == b or a == INFINITY and b != INFINITY. Otherwise, returns True if b == INFINITY or returns a < b.""" if a == LinkedDirectedGraph.INFINITY and b == LinkedDirectedGraph.INFINITY: return False elif b == LinkedDirectedGraph.INFINITY: return True elif a == LinkedDirectedGraph.INFINITY: return False else: return a < b def addWithInfinity(a, b): """If a == INFINITY or b == INFINITY, returns INFINITY. Otherwise, returns a + b.""" if a == LinkedDirectedGraph.INFINITY or b == LinkedDirectedGraph.INFINITY: return LinkedDirectedGraph.INFINITY else: return a + b def minDistance(a, b): if isLessWithInfinity(a, b): return a else: return b # Define a function that uses Floyd's algorithm def allPairsShortestPaths(matrix): """ please complete the Floyd algorithm here """ pass # Define a function to print a labeled distance matrix def printDistanceMatrix(matrix, table): """Prints the distance matrix with rows and columns labels with the index positions and vertex labels.""" labels = Array(len(table)) index = 0 labelWidth = 0 indexWidth = 0 for label in table: labels[table[label]] = label labelWidth = max(labelWidth, len(str(label))) indexWidth = max(indexWidth, len(str(index))) index += 1 weightWidth = 0 for row in range(matrix.getHeight()): for column in range(matrix.getWidth()): weightWidth = max(weightWidth, len(str(matrix[row][column]))) weightWidth = max(weightWidth, labelWidth, indexWidth) topRowLeftMargin
这段代码定义并测试 Floyd 的全部最短路径算法,使用的是文本图 12.19 中的图,该图以 example.txt 文件的形式表示。有从graph中导入LinkedDirectedGraph,从arrays中导入Array,还有随机数生成函数。
我需要一段python代码,是在一维cnn中添加灰狼优化算法
好的,我可以帮您解答这个问题。以下是关于一维CNN中添加灰狼优化算法的Python代码示例:
```python
import numpy as np
import tensorflow as tf
# 定义一维CNN模型
def oneD_CNN(x):
conv1 = tf.layers.conv1d(x, filters=32, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv2 = tf.layers.conv1d(conv1, filters=64, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
pool1 = tf.layers.max_pooling1d(conv2, pool_size=2, strides=2, padding='same')
conv3 = tf.layers.conv1d(pool1, filters=128, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv4 = tf.layers.conv1d(conv3, filters=256, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
pool2 = tf.layers.max_pooling1d(conv4, pool_size=2, strides=2, padding='same')
return pool2
# 灰狼优化算法实现
def GWO_optimizer(func, bounds, search_agents, max_iter):
dim = len(bounds)
alpha_pos = np.zeros(dim)
beta_pos = np.zeros(dim)
delta_pos = np.zeros(dim)
alpha_score = float("inf")
beta_score = float("inf")
delta_score = float("inf")
positions = np.zeros((search_agents, dim))
for i in range(dim):
positions[:, i] = np.random.uniform(bounds[i][0], bounds[i][1], search_agents)
for k in range(max_iter):
a = 2 - k * (2 / max_iter) # 更新a值
for i in range(search_agents):
for j in range(dim):
r1 = np.random.random()
r2 = np.random.random()
A1 = 2 * a * r1 - a
C1 = 2 * r2
D_alpha = abs(C1 * alpha_pos[j] - positions[i, j])
X1 = alpha_pos[j] - A1 * D_alpha
r1 = np.random.random()
r2 = np.random.random()
A2 = 2 * a * r1 - a
C2 = 2 * r2
D_beta = abs(C2 * beta_pos[j] - positions[i, j])
X2 = beta_pos[j] - A2 * D_beta
r1 = np.random.random()
r2 = np.random.random()
A3 = 2 * a * r1 - a
C3 = 2 * r2
D_delta = abs(C3 * delta_pos[j] - positions[i, j])
X3 = delta_pos[j] - A3 * D_delta
positions[i, j] = (X1 + X2 + X3) / 3
fitness = func(positions[i, :])
if fitness < alpha_score:
alpha_score = fitness
alpha_pos = positions[i, :]
if fitness > alpha_score and fitness < beta_score:
beta_score = fitness
beta_pos = positions[i, :]
if fitness > alpha_score and fitness > beta_score and fitness < delta_score:
delta_score = fitness
delta_pos = positions[i, :]
return alpha_pos
# 定义要最小化的目标函数
def target_func(x):
# 将x转换为卷积核尺度
kernel_size = int(np.round(x[0] * 7) + 3)
# 构建一维CNN模型
inputs = tf.placeholder(tf.float32, [None, 100, 1])
output = oneD_CNN(inputs)
# 构建分类层
flatten = tf.layers.flatten(output)
dense = tf.layers.dense(flatten, 256, activation=tf.nn.relu)
dropout = tf.layers.dropout(dense, rate=0.5)
logits = tf.layers.dense(dropout, 10)
# 定义损失函数和优化器
labels = tf.placeholder(tf.float32, [None, 10])
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(loss)
# 训练模型并返回损失函数值
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(100):
batch_xs, batch_ys = next_batch(128)
sess.run(train_op, feed_dict={inputs: batch_xs, labels: batch_ys})
cost = sess.run(loss, feed_dict={inputs: test_x, labels: test_y})
return cost
# 设置搜索范围和算法参数
bounds = [(0, 1)]
search_agents = 16
max_iter = 50
# 运行灰狼优化算法
best = GWO_optimizer(target_func, bounds, search_agents, max_iter)
print("Best result: ", best)
```
注:在这个示例中,我们使用灰狼优化算法来寻找最优的一维卷积核尺度,以最小化测试集上的交叉熵损失函数值。我们假设训练数据和测试数据已经被加载到变量train\_x和train\_y中,并且模型已被定义和编译。函数next\_batch用于生成batch数据。