def return_values(): #建立含有3和Python的列表L1,并将L2添加到L1后面 L2=[1,2,3,4] #********** Begin **********#
时间: 2024-03-11 12:42:16 浏览: 52
这段代码定义了一个名为return_values的函数。函数的功能是创建一个包含数字3和字符串"Python"的列表L1,并将另一个列表L2添加到L1的末尾。
下面是代码的完整实现:
```python
def return_values():
# 建立含有3和Python的列表L1,并将L2添加到L1后面
L1 = [3, "Python"]
L2 = [1, 2, 3, 4]
L1.extend(L2)
return L1
```
函数使用extend方法将L2的元素添加到L1的末尾。最后,函数返回合并后的列表L1。
相关问题
写一段python代码,读取一个csv文件,该文件没有表头,第一列为时间编号,编号以1,2,3……这样自然数的形式出现,第二列为充电量数值,将该数据集的前90%划分为训练集,其余部分设置为测试集,利用L1/L2正则化改进lstm模型,并用改进后的模型预测该时间序列,并打印rmse作为评价指标,并分别绘制训练集的真实值和预测值图片以及测试集的真实值和预测值图片,两张图片的横坐标单位为time/h,纵坐标单位为kwh
好的,下面是代码实现:
```python
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
from keras import regularizers
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
# 读取数据
data = pd.read_csv('data.csv', header=None)
values = data.values[:, 1].astype('float32') # 取第二列数据,即充电量数值
num_time_steps = len(values) # 时间序列长度
# 将数据集划分为训练集和测试集
train_size = int(num_time_steps * 0.9) # 前90%为训练集
train_data, test_data = values[:train_size], values[train_size:]
# 数据归一化
scaler = MinMaxScaler(feature_range=(0, 1))
train_data = scaler.fit_transform(train_data.reshape(-1, 1))
test_data = scaler.transform(test_data.reshape(-1, 1))
# 创建时间窗口数据
def create_dataset(data, window_size=1):
X, y = [], []
for i in range(len(data) - window_size):
X.append(data[i:(i + window_size)])
y.append(data[i + window_size])
return np.array(X), np.array(y)
window_size = 24 # 时间窗口大小为24小时
train_X, train_y = create_dataset(train_data, window_size)
test_X, test_y = create_dataset(test_data, window_size)
# 构建LSTM模型
model = Sequential()
model.add(LSTM(32, input_shape=(window_size, 1), kernel_regularizer=regularizers.l1_l2(l1=0.01, l2=0.01)))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.summary()
# 训练模型
history = model.fit(train_X, train_y, epochs=100, batch_size=64, validation_split=0.1, verbose=2)
# 预测并计算RMSE
train_predict = scaler.inverse_transform(model.predict(train_X))
test_predict = scaler.inverse_transform(model.predict(test_X))
train_rmse = np.sqrt(mean_squared_error(train_y, train_predict))
test_rmse = np.sqrt(mean_squared_error(test_y, test_predict))
print('Train RMSE: %.3f' % train_rmse)
print('Test RMSE: %.3f' % test_rmse)
# 绘制训练集预测结果
plt.figure(figsize=(10, 6))
train_predict_plot = np.empty_like(values)
train_predict_plot[:window_size] = np.nan # 前面的时间窗口数据不预测
train_predict_plot[window_size:train_size] = train_predict.reshape(-1)
plt.plot(values[:train_size], label='True Values')
plt.plot(train_predict_plot, label='Predicted Values')
plt.xlabel('Time/h')
plt.ylabel('kwh')
plt.title('Training Set')
plt.legend()
plt.show()
# 绘制测试集预测结果
plt.figure(figsize=(10, 6))
test_predict_plot = np.empty_like(values)
test_predict_plot[:train_size] = np.nan # 测试集前面的数据不预测
test_predict_plot[train_size+window_size:] = test_predict.reshape(-1)
plt.plot(values[train_size:], label='True Values')
plt.plot(test_predict_plot, label='Predicted Values')
plt.xlabel('Time/h')
plt.ylabel('kwh')
plt.title('Test Set')
plt.legend()
plt.show()
```
代码中使用了L1/L2正则化来改进LSTM模型,并采用Dropout技术来防止过拟合。在训练模型后,使用预测结果和真实值计算RMSE作为评价指标,并分别绘制出训练集和测试集的真实值和预测值的图像。
FP树和Apriori算法挖掘频繁项集的python实现
好的,我可以为您提供FP树和Apriori算法在Python中的实现。以下是示例代码:
1. FP树
```python
class TreeNode:
def __init__(self, name, count, parent):
self.name = name
self.count = count
self.parent = parent
self.children = {}
self.node_link = None
def increase(self, count):
self.count += count
def display(self, ind=1):
print(' ' * ind, self.name, ' ', self.count)
for child in self.children.values():
child.display(ind + 1)
def create_tree(data_set, min_support):
header_table = {}
for trans in data_set:
for item in trans:
header_table[item] = header_table.get(item, 0) + data_set[trans]
for k in list(header_table.keys()):
if header_table[k] < min_support:
del (header_table[k])
freq_item_set = set(header_table.keys())
if len(freq_item_set) == 0:
return None, None
for k in header_table:
header_table[k] = [header_table[k], None]
ret_tree = TreeNode('Null Set', 1, None)
for tran_set, count in data_set.items():
local_d = {}
for item in tran_set:
if item in freq_item_set:
local_d[item] = header_table[item][0]
if len(local_d) > 0:
ordered_items = [v[0] for v in sorted(local_d.items(), key=lambda x: x[1], reverse=True)]
update_tree(ordered_items, ret_tree, header_table, count)
return ret_tree, header_table
def update_tree(items, in_tree, header_table, count):
if items[0] in in_tree.children:
in_tree.children[items[0]].increase(count)
else:
in_tree.children[items[0]] = TreeNode(items[0], count, in_tree)
if header_table[items[0]][1] is None:
header_table[items[0]][1] = in_tree.children[items[0]]
else:
update_header(header_table[items[0]][1], in_tree.children[items[0]])
if len(items) > 1:
update_tree(items[1::], in_tree.children[items[0]], header_table, count)
def update_header(node_to_test, target_node):
while node_to_test.node_link is not None:
node_to_test = node_to_test.node_link
node_to_test.node_link = target_node
def ascend_tree(leaf_node, prefix_path):
if leaf_node.parent is not None:
prefix_path.append(leaf_node.name)
ascend_tree(leaf_node.parent, prefix_path)
def find_prefix_path(base_pat, tree_node):
cond_pats = {}
while tree_node is not None:
prefix_path = []
ascend_tree(tree_node, prefix_path)
if len(prefix_path) > 1:
cond_pats[frozenset(prefix_path[1:])] = tree_node.count
tree_node = tree_node.node_link
return cond_pats
def mine_tree(in_tree, header_table, min_support, pre_fix, freq_item_list):
big_l = [v[0] for v in sorted(header_table.items(), key=lambda x: x[1])]
for base_pat in big_l:
new_freq_set = pre_fix.copy()
new_freq_set.add(base_pat)
freq_item_list.append(new_freq_set)
cond_patt_bases = find_prefix_path(base_pat, header_table[base_pat][1])
my_cond_tree, my_head = create_tree(cond_patt_bases, min_support)
if my_head is not None:
mine_tree(my_cond_tree, my_head, min_support, new_freq_set, freq_item_list)
```
2. Apriori算法
```python
def load_data_set():
return [[1, 3, 4], [2, 3, 5], [1, 2, 3, 5], [2, 5]]
def create_c1(data_set):
c1 = []
for transaction in data_set:
for item in transaction:
if not [item] in c1:
c1.append([item])
c1.sort()
return list(map(frozenset, c1))
def scan_d(data_set, candidates, min_support):
ss_cnt = {}
for tid in data_set:
for can in candidates:
if can.issubset(tid):
ss_cnt[can] = ss_cnt.get(can, 0) + data_set[tid]
num_items = float(len(data_set))
ret_list = []
support_data = {}
for key in ss_cnt:
support = ss_cnt[key] / num_items
if support >= min_support:
ret_list.insert(0, key)
support_data[key] = support
return ret_list, support_data
def apriori_gen(freq_sets, k):
ret_list = []
len_lk = len(freq_sets)
for i in range(len_lk):
for j in range(i + 1, len_lk):
l1 = list(freq_sets[i])[:k - 2]
l2 = list(freq_sets[j])[:k - 2]
l1.sort()
l2.sort()
if l1 == l2:
ret_list.append(freq_sets[i] | freq_sets[j])
return ret_list
def apriori(data_set, min_support=0.5):
C1 = create_c1(data_set)
D = list(map(set, data_set))
L1, support_data = scan_d(D, C1, min_support)
L = [L1]
k = 2
while len(L[k - 2]) > 0:
Ck = apriori_gen(L[k - 2], k)
Lk, sup_k = scan_d(D, Ck, min_support)
support_data.update(sup_k)
L.append(Lk)
k += 1
return L, support_data
```
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