解释这行代码X_train = data[:test_split].drop(['datetime', 'casual', 'registered', 'count'], inplace=False, axis=1)
时间: 2024-01-25 19:01:57 浏览: 24
这行代码的作用是将数据集data中前test_split行的特定列('datetime', 'casual', 'registered', 'count')删除,并将结果赋值给X_train变量。其中,参数inplace=False表示删除操作不会在原数据集上进行修改,而是返回一个新的数据集。参数axis=1表示删除的是列而不是行。
相关问题
下面的代码哪里有问题,帮我改一下from __future__ import print_function import numpy as np import tensorflow import keras from keras.models import Sequential from keras.layers import Dense,Dropout,Flatten from keras.layers import Conv2D,MaxPooling2D from keras import backend as K import tensorflow as tf import datetime import os np.random.seed(0) from sklearn.model_selection import train_test_split from PIL import Image import matplotlib.pyplot as plt from keras.datasets import mnist images = [] labels = [] (x_train,y_train),(x_test,y_test)=mnist.load_data() X = np.array(images) print (X.shape) y = np.array(list(map(int, labels))) print (y.shape) x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0) print (x_train.shape) print (x_test.shape) print (y_train.shape) print (y_test.shape) ############################ ########## batch_size = 20 num_classes = 4 learning_rate = 0.0001 epochs = 10 img_rows,img_cols = 32 , 32 if K.image_data_format() =='channels_first': x_train =x_train.reshape(x_train.shape[0],1,img_rows,img_cols) x_test = x_test.reshape(x_test.shape[0],1,img_rows,img_cols) input_shape = (1,img_rows,img_cols) else: x_train = x_train.reshape(x_train.shape[0],img_rows,img_cols,1) x_test = x_test.reshape(x_test.shape[0],img_rows,img_cols,1) input_shape =(img_rows,img_cols,1) x_train =x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:',x_train.shape) print(x_train.shape[0],'train samples') print(x_test.shape[0],'test samples')
代码中存在以下问题:
1. 变量 images 和 labels 没有被赋值,而后面却使用了它们来构造 X 和 y,导致 X 和 y 的值都为空。
2. 导入的 mnist 数据集没有被使用,而是使用了未定义的 images 和 labels,需要将其替换为 mnist 数据集。
3. input_shape 的值应该是三元组 (img_rows, img_cols, 1),而代码中缺少了最后一个 1。
4. 在 if K.image_data_format() =='channels_first': 分支中,x_train 和 x_test 被改变了形状,但 y_train 和 y_test 却没有被改变,需要将其同步修改。
下面是修改后的代码:
```
from __future__ import print_function
import numpy as np
import tensorflow
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import tensorflow as tf
import datetime
import os
np.random.seed(0)
from sklearn.model_selection import train_test_split
from PIL import Image
import matplotlib.pyplot as plt
from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
print(x_train.shape)
print(y_train.shape)
############################
##########
batch_size = 20
num_classes = 10
learning_rate = 0.0001
epochs = 10
img_rows, img_cols = 32, 32
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (img_rows, img_cols, 1)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print('input_shape:', input_shape)
```
from sklearn.decomposition import PCA from sklearn.cluster import KMeans from sklearn.metrics import accuracy_score import numpy as np import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data import datetime # 导入数据集 start = datetime.datetime.now() #计算程序运行时间 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) X_train = mnist.train.images y_train = mnist.train.labels X_test = mnist.test.images y_test = mnist.test.labels #PCA降维 pca = PCA(n_components=10) X_train_pca = pca.fit_transform(X_train) X_test_pca = pca.fit_transform(X_test) # 可视化 plt.scatter(X_train_pca[:, 0], X_train_pca[:, 1], c=np.argmax(y_train, axis=1)) plt.show() # K-means聚类 kmeans_centers = [] # 用于存储初始类中心 for i in range(10): idx = np.where(np.argmax(y_train, axis=1) == i)[0] # 获取第i类数字的索引列表 sample_idx = np.random.choice(idx) # 随机指定一个样本作为初始类中心 kmeans_centers.append(X_train_pca[sample_idx]) # 将初始类中心添加到列表中 kmeans = KMeans(n_clusters=10,init=kmeans_centers,n_init=1) kmeans.fit(X_train_pca) # 计算分类错误率 y_pred = kmeans.predict(X_test_pca) acc = accuracy_score(np.argmax(y_test, axis=1), y_pred) print("分类错误率:{:.2%}".format(1-acc)) # 计算程序运行时间 end = datetime.datetime.now() print("程序运行时间为:"+str((end-start).seconds)+"秒")优化这段代码,输出其中pca降维的因子负荷量
可以优化代码如下:
```
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.metrics import accuracy_score
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import datetime
# 导入数据集
start = datetime.datetime.now() #计算程序运行时间
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
X_train = mnist.train.images
y_train = mnist.train.labels
X_test = mnist.test.images
y_test = mnist.test.labels
# PCA降维
pca = PCA(n_components=10)
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)
# 输出因子负荷量
print("PCA降维后的因子负荷量为:")
print(pca.components_)
# 可视化
plt.scatter(X_train_pca[:, 0], X_train_pca[:, 1], c=np.argmax(y_train, axis=1))
plt.show()
# K-means聚类
kmeans_centers = []
# 用于存储初始类中心
for i in range(10):
idx = np.where(np.argmax(y_train, axis=1) == i)[0] # 获取第i类数字的索引列表
sample_idx = np.random.choice(idx) # 随机指定一个样本作为初始类中心
kmeans_centers.append(X_train_pca[sample_idx]) # 将初始类中心添加到列表中
kmeans = KMeans(n_clusters=10,init=kmeans_centers,n_init=1)
kmeans.fit(X_train_pca)
# 计算分类错误率
y_pred = kmeans.predict(X_test_pca)
acc = accuracy_score(np.argmax(y_test, axis=1), y_pred)
print("分类错误率:{:.2%}".format(1-acc))
# 计算程序运行时间
end = datetime.datetime.now()
print("程序运行时间为:"+str((end-start).seconds)+"秒")
```
输出结果中包含了PCA降维后的因子负荷量,即`pca.components_`。