from matplotlib.font_manager import FontProperties font = FontProperties(size=14) fig = plt.figure(figsize=(10,8)) ax1 = fig.add_subplot(211) ax1.plot(pos_k) ax1.set_xlabel('正面评论LDA主题数寻优', fontproperties=font) ax2 = fig.add_subplot(212) ax2.plot(neg_k) ax2.set_xlabel('负面评论LDA主题数寻优', fontproperties=font)标题为方框,请修改代码
时间: 2023-08-05 12:09:49 浏览: 92
如果标题为方框,可能是因为你的系统中没有安装SimHei字体,或者你的代码中使用的SimHei字体文件路径不正确。你可以尝试将字体更改为其他支持中文的字体,如Microsoft YaHei、SimSun等。
以下是使用Microsoft YaHei字体的示例代码:
```
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"c:\windows\fonts\msyh.ttc", size=14)
fig = plt.figure(figsize=(10,8))
ax1 = fig.add_subplot(211)
ax1.plot(pos_k)
ax1.set_xlabel('正面评论LDA主题数寻优', fontproperties=font)
ax1.set_title('正面评论LDA主题数寻优', fontproperties=font)
ax2 = fig.add_subplot(212)
ax2.plot(neg_k)
ax2.set_xlabel('负面评论LDA主题数寻优', fontproperties=font)
ax2.set_title('负面评论LDA主题数寻优', fontproperties=font)
```
在这个代码中,我们使用了Microsoft YaHei字体来替换SimHei字体。如果你的系统中没有安装Microsoft YaHei字体,你可以更改`fname`参数为其他支持中文的字体文件路径。
相关问题
import pandas as pd import warnings import sklearn.datasets import sklearn.linear_model import matplotlib import matplotlib.font_manager as fm import matplotlib.pyplot as plt import numpy as np import seaborn as sns data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') fig = plt.figure(figsize=(10, 8)) sns.heatmap(data.corr(), cmap="YlGnBu", annot=True) plt.title('相关性分析热力图') plt.rcParams['axes.unicode_minus'] = False plt.rcParams['font.sans-serif'] = 'SimHei' plt.show() y = data['y'] X = data.drop(['y'], axis=1) print('************************输出新的特征集数据***************************') print(x.head()) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) data = np.random.rand(42, 42) data_flattened = data.flatten(data)
这段代码中存在一些错误:
1. 第一行代码应该换行,因为 Python 默认不支持在同一行中导入多个模块。
2. 导入模块的语句应该放在文件的开头,而不是在代码中间。
3. `print(x.head())` 应该改为 `print(X.head())`,因为特征集的变量名是大写的 X。
4. `data_flattened = data.flatten(data)` 应该改为 `data_flattened = data.flatten()`,因为 `numpy.ndarray.flatten()` 方法不接受参数。
下面是修改后的代码:
```
import pandas as pd
import warnings
import sklearn.datasets
import sklearn.linear_model
import matplotlib
import matplotlib.font_manager as fm
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx')
fig = plt.figure(figsize=(10, 8))
sns.heatmap(data.corr(), cmap="YlGnBu", annot=True)
plt.title('相关性分析热力图')
plt.rcParams['axes.unicode_minus'] = False
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.show()
y = data['y']
X = data.drop(['y'], axis=1)
print('************************输出新的特征集数据***************************')
print(X.head())
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
data = np.random.rand(42, 42)
data_flattened = data.flatten()
```
import pandas as pd import warnings import sklearn.datasets import sklearn.linear_model import matplotlib import matplotlib.font_manager as fm import matplotlib.pyplot as plt import numpy as np import seaborn as sns data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') print(data.info()) fig = plt.figure(figsize=(10, 8)) sns.heatmap(data.corr(), cmap="YlGnBu", annot=True) plt.title('相关性分析热力图') plt.rcParams['axes.unicode_minus'] = False plt.rcParams['font.sans-serif'] = 'SimHei' plt.show() y = data['y'] x = data.drop(['y'], axis=1) print('************************输出新的特征集数据***************************') print(x.head()) from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42) def relu(x): output=np.maximum(0, x) return output def relu_back_propagation(derror_wrt_output,x): derror_wrt_dinputs = np.array(derror_wrt_output, copy=True) derror_wrt_dinputs[x <= 0] = 0 return derror_wrt_dinputs def activated(activation_choose,x): if activation_choose == 'relu': return relu(x) def activated_back_propagation(activation_choose, derror_wrt_output, output): if activation_choose == 'relu': return relu_back_propagation(derror_wrt_output, output) class NeuralNetwork: def __init__(self, layers_strcuture, print_cost = False): self.layers_strcuture = layers_strcuture self.layers_num = len(layers_strcuture) self.param_layers_num = self.layers_num - 1 self.learning_rate = 0.0618 self.num_iterations = 2000 self.x = None self.y = None self.w = dict() self.b = dict() self.costs = [] self.print_cost = print_cost self.init_w_and_b() def set_learning_rate(self,learning_rate): self.learning_rate=learning_rate def set_num_iterations(self, num_iterations): self.num_iterations = num_iterations def set_xy(self, input, expected_output): self.x = input self.y = expected_output
这段代码存在两个问题:
1. 在import语句中没有使用逗号分隔符,应该在每个import语句之间添加逗号分隔符。
2. 在函数set_xy()的定义中,函数体没有缩进,应该在函数体内部添加缩进。
以下是修改后的代码:
```python
import pandas as pd, warnings
import sklearn.datasets, sklearn.linear_model
import matplotlib, matplotlib.font_manager as fm
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx')
print(data.info())
fig = plt.figure(figsize=(10, 8))
sns.heatmap(data.corr(), cmap="YlGnBu", annot=True)
plt.title('相关性分析热力图')
plt.rcParams['axes.unicode_minus'] = False
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.show()
y = data['y']
x = data.drop(['y'], axis=1)
print('************************输出新的特征集数据***************************')
print(x.head())
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
def relu(x):
output=np.maximum(0, x)
return output
def relu_back_propagation(derror_wrt_output,x):
derror_wrt_dinputs = np.array(derror_wrt_output, copy=True)
derror_wrt_dinputs[x <= 0] = 0
return derror_wrt_dinputs
def activated(activation_choose,x):
if activation_choose == 'relu':
return relu(x)
def activated_back_propagation(activation_choose, derror_wrt_output, output):
if activation_choose == 'relu':
return relu_back_propagation(derror_wrt_output, output)
class NeuralNetwork:
def __init__(self, layers_strcuture, print_cost = False):
self.layers_strcuture = layers_strcuture
self.layers_num = len(layers_strcuture)
self.param_layers_num = self.layers_num - 1
self.learning_rate = 0.0618
self.num_iterations = 2000
self.x = None
self.y = None
self.w = dict()
self.b = dict()
self.costs = []
self.print_cost = print_cost
self.init_w_and_b()
def set_learning_rate(self,learning_rate):
self.learning_rate=learning_rate
def set_num_iterations(self, num_iterations):
self.num_iterations = num_iterations
def set_xy(self, input, expected_output):
self.x = input
self.y = expected_output
```
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