python 中x for x in dt if x in target
时间: 2024-06-12 15:09:12 浏览: 3
在Python中,`x for x in dt if x in target`表示的是列表推导式,它根据条件筛选出目标列表`dt`中满足条件的元素,并将这些元素组成一个新的列表。具体来说,它遍历`dt`中的每个元素`x`,并检查`x`是否在目标列表`target`中,如果是,则将`x`添加到新的列表中。
相关问题
python文本中提取名词
为了在Python中提取文本中的名词,我们可以使用自然语言处理(NLP)库NLTK(Natural Language Toolkit)。引用中的代码展示了一个名词短语提取器的实现。该提取器通过查找词性标注后的文本中的名词单词,并向前、向后扩展,直到遇到非名词单词为止。具体的代码如下:
```python
import nltk
def extract_np(tagged_sent):
grammar = r'NP: {<DT>?<JJ>*<NN>}' # 定义名词短语的语法规则
cp = nltk.RegexpParser(grammar)
result = cp.parse(tagged_sent)
return result
nps = []
tagged_tokens = nltk.pos_tag(tokens) # 对文本进行词性标注
for tagged_sent in tagged_tokens:
tree = extract_np(tagged_sent) # 提取名词短语
for subtree in tree.subtrees():
if subtree.label() == 'NP':
t = subtree
t = ' '.join(word for word, tag in t.leaves()) # 将名词短语拼接成字符串
nps.append(t)
```
以上代码首先使用`nltk.pos_tag()`函数对文本进行词性标注,然后将标注结果传递给`extract_np()`函数进行名词短语提取。最后,将提取得到的名词短语存储在列表`nps`中。请注意,这只是提取名词的一种方法,还可以根据实际需求进行调整和改进。引用和提供了关于NLP和名词短语提取的更详细的信息。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [chatgpt赋能python:Python提取名词:利用NLP技术进行文本分析的高效方法](https://blog.csdn.net/laingziwei/article/details/131319121)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
- *2* *3* [Python实现基于自然语言处理工具NLTK的名词短语提取器](https://blog.csdn.net/Jack_user/article/details/130976216)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
[ .reference_list ]
决策树算法python实现
以下是一个简单的决策树算法的Python实现:
1. 导入必要的库
```python
import pandas as pd
import numpy as np
```
2. 定义节点类
```python
class Node:
def __init__(self, feature=None, threshold=None, left=None, right=None, value=None):
self.feature = feature
self.threshold = threshold
self.left = left
self.right = right
self.value = value
```
3. 定义决策树类
```python
class DecisionTree:
def __init__(self, max_depth=None):
self.max_depth = max_depth
def fit(self, X, y):
self.n_features_ = X.shape[1]
self.tree_ = self._grow_tree(X, y)
def predict(self, X):
return [self._predict(inputs) for inputs in X]
def _best_split(self, X, y):
m = y.size
if m <= 1:
return None, None
num_parent = [np.sum(y == c) for c in range(self.n_classes_)]
best_gini = 1.0 - sum((n / m) ** 2 for n in num_parent)
best_idx, best_thr = None, None
for idx in range(self.n_features_):
thresholds, classes = zip(*sorted(zip(X[:, idx], y)))
num_left = [0] * self.n_classes_
num_right = num_parent.copy()
for i in range(1, m):
c = classes[i - 1]
num_left[c] += 1
num_right[c] -= 1
gini_left = 1.0 - sum((num_left[x] / i) ** 2 for x in range(self.n_classes_))
gini_right = 1.0 - sum((num_right[x] / (m - i)) ** 2 for x in range(self.n_classes_))
gini = (i * gini_left + (m - i) * gini_right) / m
if thresholds[i] == thresholds[i - 1]:
continue
if gini < best_gini:
best_gini = gini
best_idx = idx
best_thr = (thresholds[i] + thresholds[i - 1]) / 2
return best_idx, best_thr
def _grow_tree(self, X, y, depth=0):
num_samples_per_class = [np.sum(y == i) for i in range(self.n_classes_)]
predicted_class = np.argmax(num_samples_per_class)
node = Node(value=predicted_class)
if depth < self.max_depth:
idx, thr = self._best_split(X, y)
if idx is not None:
indices_left = X[:, idx] < thr
X_left, y_left = X[indices_left], y[indices_left]
X_right, y_right = X[~indices_left], y[~indices_left]
node.feature = idx
node.threshold = thr
node.left = self._grow_tree(X_left, y_left, depth + 1)
node.right = self._grow_tree(X_right, y_right, depth + 1)
return node
def _predict(self, inputs):
node = self.tree_
while node.left:
if inputs[node.feature] < node.threshold:
node = node.left
else:
node = node.right
return node.value
```
4. 测试算法
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
dt = DecisionTree(max_depth=4)
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
```
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