解释df_noise['DP_news'][i]+=np.random.normal(0,noise*news_var)
时间: 2024-03-04 18:50:51 浏览: 25
这是一行Python代码,其中调用了NumPy库中的random.normal函数。该行代码的作用是给数据框df_noise中列名为'DP_news'的第i个元素加上一个服从均值为0、标准差为noise*news_var的正态分布的随机噪声。
具体来说,np.random.normal(0, noise*news_var)生成一个服从均值为0、标准差为noise*news_var的正态分布的随机数。然后,这个随机数被加到df_noise['DP_news'][i]上,从而得到新的值并更新该元素。这个过程模拟了在DP_news这个变量中加入噪声的情况,其中noise和news_var是预先设定好的参数,用来控制噪声的大小和分布的方差。
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class RNN: def init(self, input_size, hidden_size, output_size): self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size # 初始化参数 self.Wxh = np.random.randn(hidden_size, input_size) * 0.01 # 输入层到隐藏层的权重矩阵 self.Whh = np.random.randn(hidden_size, hidden_size) * 0.01 # 隐藏层到隐藏层的权重矩阵 self.Why = np.random.randn(output_size, hidden_size) * 0.01 # 隐藏层到输出层的权重矩阵 self.bh = np.zeros((hidden_size, 1)) # 隐藏层偏置 self.by = np.zeros((output_size, 1)) # 输出层偏置 # 初始化隐藏状态 self.h = np.zeros((hidden_size, 1)) def forward(self, x): # 更新隐藏状态 self.h = np.tanh(np.dot(self.Wxh, x) + np.dot(self.Whh, self.h) + self.bh) # 计算输出 y = np.dot(self.Why, self.h) + self.by # 返回输出和隐藏状态 return y, self.h def backward(self, x, y, target, learning_rate): # 计算输出误差 dy = y - target # 计算隐藏状态误差 dh = np.dot(self.Why.T, dy) * (1 - self.h ** 2) # 计算权重和偏置的梯度 dWhy = np.dot(dy, self.h.T) dby = np.sum(dy, axis=1, keepdims=True) dWxh = np.dot(dh, x.T) dWhh = np.dot(dh, self.h.T) dbh = np.sum(dh, axis=1, keepdims=True) # 更新权重和偏置 self.Why -= learning_rate * dWhy self.by -= learning_rate * dby self.Wxh -= learning_rate * dWxh self.Whh -= learning_rate * dWhh self.bh -= learning_rate * dbh 帮写一下用online_shopping_10_cats数据集训练以上模型train函数以及test函数
好的,这里是使用online_shopping_10_cats数据集训练以上模型的train函数以及test函数:
```python
import numpy as np
import pandas as pd
import string
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from sklearn.model_selection import train_test_split
# 加载数据集
df = pd.read_csv("online_shopping_10_cats.csv")
df = df[["Review Text", "Cat1"]]
df = df.dropna()
# 数据预处理
stop_words = set(stopwords.words('english') + list(string.punctuation))
def preprocess_text(text):
tokens = word_tokenize(text.lower())
tokens = [t for t in tokens if t not in stop_words]
return " ".join(tokens)
df["Review Text"] = df["Review Text"].apply(preprocess_text)
# 划分训练集和测试集
train_texts, test_texts, train_labels, test_labels = train_test_split(df["Review Text"], df["Cat1"], test_size=0.2)
# 构建词典
word_to_index = {}
index_to_word = {}
for i, word in enumerate(set(" ".join(train_texts).split())):
word_to_index[word] = i
index_to_word[i] = word
# 将文本转换为数字序列
def text_to_sequence(text):
seq = []
for word in text.split():
seq.append(word_to_index[word])
return seq
train_sequences = [text_to_sequence(text) for text in train_texts]
test_sequences = [text_to_sequence(text) for text in test_texts]
# 将标签转换为数字
label_to_index = {}
index_to_label = {}
for i, label in enumerate(set(train_labels)):
label_to_index[label] = i
index_to_label[i] = label
train_labels = [label_to_index[label] for label in train_labels]
test_labels = [label_to_index[label] for label in test_labels]
# 定义 RNN 模型
class RNN:
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
# 初始化参数
self.Wxh = np.random.randn(hidden_size, input_size) * 0.01 # 输入层到隐藏层的权重矩阵
self.Whh = np.random.randn(hidden_size, hidden_size) * 0.01 # 隐藏层到隐藏层的权重矩阵
self.Why = np.random.randn(output_size, hidden_size) * 0.01 # 隐藏层到输出层的权重矩阵
self.bh = np.zeros((hidden_size, 1)) # 隐藏层偏置
self.by = np.zeros((output_size, 1)) # 输出层偏置
# 初始化隐藏状态
self.h = np.zeros((hidden_size, 1))
def forward(self, x):
# 更新隐藏状态
self.h = np.tanh(np.dot(self.Wxh, x) + np.dot(self.Whh, self.h) + self.bh)
# 计算输出
y = np.dot(self.Why, self.h) + self.by
# 返回输出和隐藏状态
return y, self.h
def backward(self, x, y, target, learning_rate):
# 计算输出误差
dy = y - target
# 计算隐藏状态误差
dh = np.dot(self.Why.T, dy) * (1 - self.h ** 2)
# 计算权重和偏置的梯度
dWhy = np.dot(dy, self.h.T)
dby = np.sum(dy, axis=1, keepdims=True)
dWxh = np.dot(dh, x.T)
dWhh = np.dot(dh, self.h.T)
dbh = np.sum(dh, axis=1, keepdims=True)
# 更新权重和偏置
self.Why -= learning_rate * dWhy
self.by -= learning_rate * dby
self.Wxh -= learning_rate * dWxh
self.Whh -= learning_rate * dWhh
self.bh -= learning_rate * dbh
# 训练函数
def train(model, sequences, labels, learning_rate, epochs):
for epoch in range(epochs):
loss = 0
for i in range(len(sequences)):
# 将输入和输出转换为 one-hot 编码
x = np.zeros((len(sequences[i]), model.input_size))
for j, index in enumerate(sequences[i]):
x[j, index] = 1
y = np.zeros((model.output_size, 1))
y[labels[i]] = 1
# 前向传播
output, hidden = model.forward(x.T)
# 计算损失
loss += np.sum((output - y) ** 2)
# 反向传播
model.backward(x.T, output, y, learning_rate)
# 输出每个 epoch 的损失
print("Epoch {}/{} loss: {}".format(epoch + 1, epochs, loss / len(sequences)))
# 测试函数
def test(model, sequences, labels):
correct = 0
for i in range(len(sequences)):
# 将输入转换为 one-hot 编码
x = np.zeros((len(sequences[i]), model.input_size))
for j, index in enumerate(sequences[i]):
x[j, index] = 1
# 前向传播
output, hidden = model.forward(x.T)
# 获取预测结果
prediction = np.argmax(output)
# 更新正确预测的数量
if prediction == labels[i]:
correct += 1
# 输出准确率
accuracy = correct / len(sequences)
print("Accuracy: {}".format(accuracy))
# 实例化 RNN 模型
input_size = len(word_to_index)
hidden_size = 64
output_size = len(label_to_index)
model = RNN(input_size, hidden_size, output_size)
# 训练模型
learning_rate = 0.01
epochs = 10
train(model, train_sequences, train_labels, learning_rate, epochs)
# 测试模型
test(model, test_sequences, test_labels)
```
在训练模型时,我们使用了 online_shopping_10_cats 数据集,并对数据进行了预处理、划分训练集和测试集、构建词典等操作。在训练过程中,我们使用了前向传播和反向传播算法来更新模型的权重和偏置,并输出了每个 epoch 的损失。在测试模型时,我们使用了测试集,计算出了模型的准确率。
import numpy as np import pandas as pd def localmin(points, pixel_size): x_min = np.min(points[:, 0]) y_min = np.min(points[:, 1]) x_max = np.max(points[:, 0]) y_max = np.max(points[:, 1]) w = x_max - x_min h = y_max - y_min wn = w // pixel_size + 1 hn = h // pixel_size + 1 x_bins = np.array([i * pixel_size for i in range(int(wn + 1))]) y_bins = np.array([i * pixel_size for i in range(int(hn + 1))]) df = pd.DataFrame(points, columns=['x', 'y', 'z', 'classification', 'indices']) df['x_bin'] = pd.cut(df['x'], bins=x_bins) df['y_bin'] = pd.cut(df['y'], bins=y_bins) result = df.groupby(['x_bin', 'y_bin']).apply(lambda x: x.loc[x['z'].idxmin()])[['x', 'y', 'z', 'classification', 'indices']] return result a = np.random.random([100,3]) b = np.random.random([100, 1])//0.5 c = np.arange(1000).reshape([100,1]) a = np.concatenate([a,b,c],axis=-1) d = localmin(a,0.2) 如何从a中删除d中的点
可以使用pandas的isin()方法来判断a中的点是否出现在d中,然后使用~运算符将不包含在d中的点筛选出来。具体实现代码如下:
```python
import numpy as np
import pandas as pd
def localmin(points, pixel_size):
x_min = np.min(points[:, 0])
y_min = np.min(points[:, 1])
x_max = np.max(points[:, 0])
y_max = np.max(points[:, 1])
w = x_max - x_min
h = y_max - y_min
wn = w // pixel_size + 1
hn = h // pixel_size + 1
x_bins = np.array([i * pixel_size for i in range(int(wn + 1))])
y_bins = np.array([i * pixel_size for i in range(int(hn + 1))])
df = pd.DataFrame(points, columns=['x', 'y', 'z', 'classification', 'indices'])
df['x_bin'] = pd.cut(df['x'], bins=x_bins)
df['y_bin'] = pd.cut(df['y'], bins=y_bins)
result = df.groupby(['x_bin', 'y_bin']).apply(lambda x: x.loc[x['z'].idxmin()])[['x', 'y', 'z', 'classification', 'indices']]
return result
a = np.random.random([100,3])
b = np.random.random([100, 1])//0.5
c = np.arange(1000).reshape([100,1])
a = np.concatenate([a,b,c],axis=-1)
d = localmin(a,0.2)
# 筛选出不包含在d中的点并重新赋值给a
a = a[~a[:, :2].isin(d.iloc[:, :2].values).any(axis=1)]
```
其中,isin()方法用于判断一个DataFrame或Series对象中的值是否在另一个DataFrame或Series对象中出现过。在这里我们用isin()方法来判断a中的前两列(即x和y坐标)是否出现在d中,然后使用any()方法将结果沿着列方向合并,得到一个长度为100的布尔型数组,表示a中每个点是否在d中出现过。最后使用~运算符对数组进行取反,得到不包含在d中的点的布尔型数组,然后使用该数组对a进行筛选即可。