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leecode更新

This commit is contained in:
markilue 2022-10-21 13:41:39 +08:00
parent 1b92547add
commit 10c949ab6c
7 changed files with 627 additions and 60 deletions
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13
Big_data_example/user_profile/export-clickhouse/pom.xml
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@ -17,6 +17,19 @@
<groupId>com.atguigu</groupId>
<version>1.0-SNAPSHOT</version>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.13.2</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.13.2</version>
<scope>compile</scope>
</dependency>
</dependencies>

3
Big_data_example/user_profile/export-clickhouse/src/main/resources/config.properties
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@ -8,4 +8,5 @@ mysql.username=root
mysql.password=123456
# clickhouseÅäÖÃ
clickhouse.url=jdbc:clickhouse://Ding202:8123/user_profile0224
#clickhouse.url=jdbc:clickhouse://Ding202:8123/user_profile0224
clickhouse.url=jdbc:clickhouse://localhost:8123/default

44
Big_data_example/user_profile/export-clickhouse/src/test/java/clickhouseJDBC.scala Normal file
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@ -0,0 +1,44 @@
import com.atguigu.userprofile.common.utils.ClickhouseUtils
object clickhouseJDBC {
def main(args: Array[String]): Unit = {
val createSQL =
s"""
|create table t_order_mt(
| uid UInt32,
| sku_id String,
| total_amount Decimal(16,2),
| create_time Datetime
| ) engine =MergeTree
| partition by toYYYYMMDD(create_time)
| primary key (uid)
| order by (uid,sku_id)
|""".stripMargin
ClickhouseUtils.executeSql(createSQL)
val insertSQL=
"""
|insert into t_order_mt
|values(101,'sku_001',1000.00,'2020-06-01 12:00:00') ,
|(102,'sku_002',2000.00,'2020-06-01 11:00:00'),
|(102,'sku_004',2500.00,'2020-06-01 12:00:00'),
|(102,'sku_002',2000.00,'2020-06-01 13:00:00')
|(102,'sku_002',12000.00,'2020-06-01 13:00:00')
|(102,'sku_002',600.00,'2020-06-02 12:00:00')
|""".stripMargin
ClickhouseUtils.executeSql(insertSQL)
val selectSQL=
"""
|select * from t_order_mt
|""".stripMargin
ClickhouseUtils.executeSql(selectSQL)
}
}

20
Big_data_example/user_profile/export-clickhouse/src/test/java/clickhouseJDNC.java Normal file
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@ -0,0 +1,20 @@
import com.atguigu.userprofile.common.utils.ClickhouseUtils;
import org.junit.Test;
/**
* @BelongsProject: user-profile-manager0111
* @BelongsPackage: PACKAGE_NAME
* @Author: markilue
* @CreateTime: 2022-10-20 16:58
* @Description: TODO 尝试连接clickhouse
* @Version: 1.0
*/
public class clickhouseJDNC {
@Test
public void testClickhouse(){
}
}

285
Leecode/src/main/java/com/markilue/leecode/backtrace/SolveSudoku.java Normal file
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@ -0,0 +1,285 @@
package com.markilue.leecode.backtrace;
import org.junit.Test;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
/**
* @BelongsProject: Leecode
* @BelongsPackage: com.markilue.leecode.backtrace
* @Author: markilue
* @CreateTime: 2022-10-21 10:11
* @Description: TODO 力扣37题 解数独：
* 编写一个程序，通过填充空格来解决数独问题。
* 数独的解法需 遵循如下规则：
* 数字 1-9 在每一行只能出现一次。
* 数字 1-9 在每一列只能出现一次。
* 数字 1-9 在每一个以粗实线分隔的 3x3 宫内只能出现一次。（请参考示例图）
* 数独部分空格内已填入了数字，空白格用 '.' 表示。
* @Version: 1.0
*/
public class SolveSudoku {
@Test
public void test() {
char a = '1';
char[][] board = {
{'5', '3', '.', '.', '7', '.', '.', '.', '.'},
{'6', '.', '.', '1', '9', '5', '.', '.', '.'},
{'.', '9', '8', '.', '.', '.', '.', '6', '.'},
{'8', '.', '.', '.', '6', '.', '.', '.', '3'},
{'4', '.', '.', '8', '.', '3', '.', '.', '1'},
{'7', '.', '.', '.', '2', '.', '.', '.', '6'},
{'.', '6', '.', '.', '.', '.', '2', '8', '.'},
{'.', '.', '.', '4', '1', '9', '.', '.', '5'},
{'.', '.', '.', '.', '8', '.', '.', '7', '9'}};
solveSudoku(board);
for (int i = 0; i < board.length; i++) {
System.out.println(Arrays.toString(board[i]));
}
}
public void solveSudoku(char[][] board) {
backtracking1(board);
}
/**
* 自己思路：填写i,j位置的值
* 存在问题，似乎存在问题，出错之后会一直return回溯了？
* @param board
* @param i
* @param j
*/
public void backtracking(char[][] board, int i, int j) {
if (i == board.length) {
return;
}
if (j == board.length) {
backtracking(board, i + 1, 0);
return;
}
if(board[i][j]!='.'){
backtracking(board,i,j+1);
return;
}
for (int k = 1; k <= 9; k++) {
if (!check(board, i, j, k)) {
continue;
}
board[i][j] = (char) ('0' + k);
backtracking(board, i, j + 1);
// board[i][j] = '.';
}
}
/**
* 代码随想录思路：
* 速度击败6.41%，内存击败65.83%
* @param board
* @return
*/
public boolean backtracking1(char[][] board) {
for (int i = 0; i < board.length; i++) {
for (int j = 0; j < board[0].length; j++) {
if(board[i][j]!='.')continue;
for (char k = '1'; k <= '9'; k++) {
if(check1(board,i,j,k)){
board[i][j]=k;
//找到第一个直接return
if(backtracking1(board))return true;
board[i][j]='.'; //回溯撤销k
}
}
return false;
}
}
return true; //遍历完了都没有返回，那么就说明找到了合适的位置
}
/**
* i,j分别是需要填写的横坐标和纵坐标，value是要填入的值
*
* @param board
* @param i
* @param j
* @return
*/
public boolean check1(char[][] board, int i, int j, char value) {
// //测试当前位置
// if (board[i][j] != '.') {
// return false;
// }
//监测横轴
for (int k = 0; k < board[i].length; k++) {
if (board[i][k] == value) {
return false;
}
}
//监测纵轴
for (int k = 0; k < board[j].length; k++) {
if ( board[k][j] == value) {
return false;
}
}
//监测九宫格
int a = (i / 3)*3;
int b = (j / 3)*3;
for (int k = a; k < a+2; k++) {
for (int l = b; l <= b+2; l++) {
if ( board[k][l] == value) {
return false;
}
}
}
return true;
}
/**
* i,j分别是需要填写的横坐标和纵坐标，value是要填入的值
*
* @param board
* @param i
* @param j
* @return
*/
public boolean check(char[][] board, int i, int j, int value) {
// //测试当前位置
// if (board[i][j] != '.') {
// return false;
// }
//监测横轴
for (int k = 0; k < board[i].length; k++) {
if (board[i][k] != '.' && board[i][k] - '0' == value) {
return false;
}
}
//监测纵轴
for (int k = 0; k < board[j].length; k++) {
if (board[k][j] != '.' && board[k][j] - '0' == value) {
return false;
}
}
//监测九宫格
int a = i % 3;
int b = j % 3;
int xstart = 0;
int xend = 0;
int ystart = 0;
int yend = 0;
if (a == 0) {
xstart = i + 1;
xend = i + 2;
} else if (a == 1) {
xstart = i - 1;
xend = i + 1;
} else {
xstart = i - 2;
xend = i - 1;
}
if (b == 0) {
ystart = j + 1;
yend = j + 2;
} else if (b == 1) {
ystart = j - 1;
yend = j + 1;
} else {
ystart = j - 2;
yend = j - 1;
}
for (int k = xstart; k <= xend; k++) {
for (int l = ystart; l <= yend; l++) {
if (board[k][l] != '.' && board[k][l] - '0' == value) {
return false;
}
}
}
return true;
}
/**
* 官方回溯法：本质上就是记录各个位置使用的数组的情况，然后根据条件：
* line[i][digit] = column[j][digit] = block[i / 3][j / 3][digit] = false才能填入
* 需要填写的位置通过spaces记录下来，然后按个去填写
* 官方还有两种优化方式：位运算方式和枚举优化，但是都不是算法层面的优化，此处不一一写
* 位运算优化：分别是使用int[i]=k数组去记录位置i填入的数字k
* 枚举优化：如果一个空白格只有唯一的数可以填入，也就是其对应的 bb 值和 b-1b−1 进行按位与运算后得到 00（即 bb 中只有一个二进制位为 11）。此时，我们就可以确定这个空白格填入的数，而不用等到递归时再去处理它。
*
*/
private boolean[][] line = new boolean[9][9];
private boolean[][] column = new boolean[9][9];
private boolean[][][] block = new boolean[3][3][9];
private boolean valid = false;
private List<int[]> spaces = new ArrayList<int[]>();
public void solveSudoku1(char[][] board) {
for (int i = 0; i < 9; ++i) {
for (int j = 0; j < 9; ++j) {
if (board[i][j] == '.') {
spaces.add(new int[]{i, j});
} else {
int digit = board[i][j] - '0' - 1;
line[i][digit] = column[j][digit] = block[i / 3][j / 3][digit] = true;
}
}
}
dfs(board, 0);
}
public void dfs(char[][] board, int pos) {
if (pos == spaces.size()) {
valid = true;
return;
}
int[] space = spaces.get(pos);
int i = space[0], j = space[1];
for (int digit = 0; digit < 9 && !valid; ++digit) {
if (!line[i][digit] && !column[j][digit] && !block[i / 3][j / 3][digit]) {
line[i][digit] = column[j][digit] = block[i / 3][j / 3][digit] = true;
board[i][j] = (char) (digit + '0' + 1);
dfs(board, pos + 1);
line[i][digit] = column[j][digit] = block[i / 3][j / 3][digit] = false;
}
}
}
}

241
TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/resnet_18.py
View File

@ -21,7 +21,7 @@ K = 18
namuda = 0.01
'''保存名称'''
save_name = "./model//{0}.h5".format(model_name,
save_name = "./model/{0}.h5".format(model_name,
time_stamp,
feature_num,
batch_size,
@ -32,12 +32,12 @@ save_step_two_name = "./model/two_weight/{0}_weight_epoch6_99899_9996/weight".fo
batch_size,
EPOCH)
save_mse_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
save_mse_name = "./mse/ResNet/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
time_stamp,
feature_num,
batch_size,
EPOCH)
save_max_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
save_max_name = "./mse/ResNet/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
time_stamp,
feature_num,
batch_size,
@ -220,11 +220,134 @@ def resnet_Model():
dense = tf.keras.layers.Dense(128, activation=tf.nn.relu)(dropout)
dense = tf.keras.layers.BatchNormalization(name="bn_last")(dense)
dense = tf.keras.layers.Dense(2, activation=tf.nn.sigmoid)(dense)
dense = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)(dense)
model = tf.keras.Model(inputs=inputs, outputs=dense)
return model
def showResult(step_two_model: tf.keras.Model, test_data, isPlot: bool = False, isSave: bool = True):
# 获取模型的所有参数的个数
# step_two_model.count_params()
total_result = []
size, length, dims = test_data.shape
predict_label = step_two_model.predict(test_data)
total_result = np.reshape(total_result, [total_result.__len__(), -1])
total_result = np.reshape(total_result, [-1, ])
# 误报率，漏报率，准确性的计算
if isSave:
np.savetxt(save_mse_name, total_result, delimiter=',')
if isPlot:
plt.figure(1, figsize=(6.0, 2.68))
plt.subplots_adjust(left=0.1, right=0.94, bottom=0.2, top=0.9, wspace=None,
hspace=None)
plt.tight_layout()
font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 10} # 设置坐标标签的字体大小，字体
plt.scatter(list(range(total_result.shape[0])), total_result, c='black', s=10)
# 画出 y=1 这条水平线
plt.axhline(0.5, c='red', label='Failure threshold')
# 箭头指向上面的水平线
# plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
# alpha=0.9, overhang=0.5)
plt.text(test_data.shape[0] * 2 / 3 + 1000, 0.7, "Truth Fault", fontsize=10, color='red',
verticalalignment='top')
plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
plt.xticks(range(6), ('06/09/17', '12/09/17', '18/09/17', '24/09/17', '29/09/17')) # 设置x轴的标尺
plt.tick_params() # 设置轴显示
plt.xlabel("time", fontdict=font1)
plt.ylabel("confience", fontdict=font1)
plt.text(total_result.shape[0] * 4 / 5, 0.6, "Fault", fontsize=10, color='black', verticalalignment='top',
horizontalalignment='center',
bbox={'facecolor': 'grey',
'pad': 10}, fontdict=font1)
plt.text(total_result.shape[0] * 1 / 3, 0.4, "Norm", fontsize=10, color='black', verticalalignment='top',
horizontalalignment='center',
bbox={'facecolor': 'grey',
'pad': 10}, fontdict=font1)
plt.grid()
# plt.ylim(0, 1)
# plt.xlim(-50, 1300)
# plt.legend("", loc='upper left')
plt.show()
return total_result
def plot_result(result_data):
parameters = {
'figure.dpi': 600,
'figure.figsize': (5, 5),
'savefig.dpi': 600,
'xtick.direction': 'in',
'ytick.direction': 'in',
'xtick.labelsize': 20,
'ytick.labelsize': 20,
'legend.fontsize': 11.3,
}
plt.rcParams.update(parameters)
plt.figure()
plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵
plt.scatter(list(range(result_data.shape[0])), result_data, c='black', s=10)
# 画出 y=1 这条水平线
plt.axhline(0.5, c='red', label='Failure threshold')
# 箭头指向上面的水平线
# plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
# alpha=0.9, overhang=0.5)
plt.text(test_data.shape[0] * 2 / 3 + 1000, 0.7, "Truth Fault", fontsize=10, color='red',
verticalalignment='top')
plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
plt.xticks(range(6), ('06/09/17', '12/09/17', '18/09/17', '24/09/17', '29/09/17')) # 设置x轴的标尺
indices = range(result_data.shape[0])
classes = ['N', 'IF', 'OF' 'TRC', 'TSP'] # for i in range(cls):
# 第一个是迭代对象，表示坐标的显示顺序，第二个参数是坐标轴显示列表
# plt.xticks(indices. classes,rotation=45)#设置横坐标方向，rotation=45为45度倾斜# plt.yticks(indices, classes, rotation=45)
plt.xticks([index + 0.5 for index in indices], classes, rotation=45) # 设置横坐标方向，rotation=45为45度倾斜
# plt.yticks([index + 0.5 for index in indices], classes, rotation=45)
plt.ylabel('Actual label', fontsize=20)
plt.xlabel('Predicted label', fontsize=20)
plt.tight_layout()
plt.show()
pass
def plot_confusion_matrix_accuracy(cls, true_labels, predict_labels):
sns.set(font_scale=1.5)
parameters = {
'figure.dpi': 600,
'figure.figsize': (5, 5),
'savefig.dpi': 600,
'xtick.direction': 'in',
'ytick.direction': 'in',
'xtick.labelsize': 20,
'ytick.labelsize': 20,
'legend.fontsize': 11.3,
}
plt.rcParams.update(parameters)
plt.figure()
plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵
confusion = confusion_matrix(true_labels, predict_labels)
# confusion = confusion.astype('float') / confusion.sum(axis=1)[: np.newaxis] plt.figure()
# sns.heatmap(confusion, annot=True,fmt="d",cmap="Greens")
sns.heatmap(confusion, annot=True, fmt="d", cmap="Blues", vmax=100, vmin=0, cbar=None, square=True)
indices = range(len(confusion))
classes = ['N', 'IF', 'OF' 'TRC', 'TSP'] # for i in range(cls):
# classes.append(str(i))
# 第一个是迭代对象，表示坐标的显示顺序，第二个参数是坐标轴显示列表
# plt.xticks(indices. classes,rotation=45)#设置横坐标方向，rotation=45为45度倾斜# plt.yticks(indices, classes, rotation=45)
plt.xticks([index + 0.5 for index in indices], classes, rotation=45) # 设置横坐标方向，rotation=45为45度倾斜
plt.yticks([index + 0.5 for index in indices], classes, rotation=45)
plt.ylabel('Actual label', fontsize=20)
plt.xlabel('Predicted label', fontsize=20)
plt.tight_layout()
plt.show()
if __name__ == '__main__':
# # 数据读入
#
@ -251,60 +374,68 @@ if __name__ == '__main__':
model.compile(optimizer=tf.optimizers.Adam(), loss=tf.losses.binary_crossentropy,
metrics=['acc'])
model.summary()
early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=5, mode='min', verbose=1)
checkpoint = tf.keras.callbacks.ModelCheckpoint(
filepath=save_name,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min',
period=1)
history = model.fit(train_data, train_label, epochs=20, batch_size=32, validation_data=(test_data, test_label),
callbacks=[checkpoint, early_stop])
model.save("./model/ResNet.h5")
model = tf.keras.models.load_model("../model/ResNet_model.h5")
# early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=5, mode='min', verbose=1)
#
# checkpoint = tf.keras.callbacks.ModelCheckpoint(
# filepath=save_name,
# monitor='val_loss',
# verbose=1,
# save_best_only=False,
# mode='min',
# period=1)
#
# history = model.fit(train_data, train_label, epochs=20, batch_size=16, validation_data=(test_data, test_label),
# callbacks=[checkpoint, early_stop])
# model.save("./model/ResNet.h5")
model = tf.keras.models.load_model("./model/ResNet_2_9906.h5")
# 结果展示
trained_data = tf.keras.models.Model(inputs=model.input, outputs=model.get_layer('bn_last').output).predict(
train_data)
predict_label = model.predict(test_data)
predict_label_max = np.argmax(predict_label, axis=1)
predict_label = np.expand_dims(predict_label_max, axis=1)
confusion_matrix = confusion_matrix(test_label, predict_label)
healthy_size, _, _ = train_data_healthy.shape
unhealthy_size, _, _ = train_data_unhealthy.shape
all_data, _, _ = get_training_data_overlapping(
total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
tsne = TSNE(n_components=3, verbose=2, perplexity=30, n_iter=5000).fit_transform(trained_data)
print("tsne[:,0]", tsne[:, 0])
print("tsne[:,1]", tsne[:, 1])
print("tsne[:,2]", tsne[:, 2])
x, y, z = tsne[:, 0], tsne[:, 1], tsne[:, 2]
x = (x - np.min(x)) / (np.max(x) - np.min(x))
y = (y - np.min(y)) / (np.max(y) - np.min(y))
z = (z - np.min(z)) / (np.max(z) - np.min(z))
showResult(step_two_model, test_data=all_data, isPlot=True)
fig1 = plt.figure()
ax1 = fig1.add_subplot(projection='3d')
ax1.scatter3D(x, y, z, c=train_label, cmap=plt.cm.get_cmap("jet", 10))
fig2 = plt.figure()
ax2 = fig2.add_subplot()
sns.heatmap(confusion_matrix, annot=True, fmt="d", cmap='Blues')
plt.ylabel('Actual label')
plt.xlabel('Predicted label')
# fig3 = plt.figure()
# ax3 = fig3.add_subplot()
# plt.plot(history.epoch, history.history.get('acc'), label='acc')
# plt.plot(history.epoch, history.history.get('val_acc'), label='val_acc')
# trained_data = tf.keras.models.Model(inputs=model.input, outputs=model.get_layer('bn_last').output).predict(
# train_data)
# predict_label = model.predict(test_data)
# predict_label_max = np.argmax(predict_label, axis=1)
# predict_label = np.expand_dims(predict_label_max, axis=1)
#
# fig4 = plt.figure()
# ax4 = fig3.add_subplot()
# plt.plot(history.epoch, history.history.get('loss'), label='loss')
# plt.plot(history.epoch, history.history.get('val_loss'), label='val_loss')
plt.legend()
plt.show()
score = model.evaluate(test_data, test_label)
print('score:', score)
# confusion_matrix = confusion_matrix(test_label, predict_label)
#
# tsne = TSNE(n_components=3, verbose=2, perplexity=30, n_iter=5000).fit_transform(trained_data)
# print("tsne[:,0]", tsne[:, 0])
# print("tsne[:,1]", tsne[:, 1])
# print("tsne[:,2]", tsne[:, 2])
# x, y, z = tsne[:, 0], tsne[:, 1], tsne[:, 2]
# x = (x - np.min(x)) / (np.max(x) - np.min(x))
# y = (y - np.min(y)) / (np.max(y) - np.min(y))
# z = (z - np.min(z)) / (np.max(z) - np.min(z))
#
# fig1 = plt.figure()
# ax1 = fig1.add_subplot(projection='3d')
# ax1.scatter3D(x, y, z, c=train_label, cmap=plt.cm.get_cmap("jet", 10))
#
# fig2 = plt.figure()
# ax2 = fig2.add_subplot()
# sns.heatmap(confusion_matrix, annot=True, fmt="d", cmap='Blues')
# plt.ylabel('Actual label')
# plt.xlabel('Predicted label')
#
# # fig3 = plt.figure()
# # ax3 = fig3.add_subplot()
# # plt.plot(history.epoch, history.history.get('acc'), label='acc')
# # plt.plot(history.epoch, history.history.get('val_acc'), label='val_acc')
# #
# # fig4 = plt.figure()
# # ax4 = fig3.add_subplot()
# # plt.plot(history.epoch, history.history.get('loss'), label='loss')
# # plt.plot(history.epoch, history.history.get('val_loss'), label='val_loss')
# plt.legend()
# plt.show()
#
# score = model.evaluate(test_data, test_label)
# print('score:', score)

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TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py Normal file
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# -*- coding: utf-8 -*-
# coding: utf-8
import matplotlib.pyplot as plt
import numpy as np
'''
@Author : dingjiawen
@Date : 2022/10/20 21:35
@Usage :
@Desc :
'''
def plot_result(result_data):
parameters = {
'figure.dpi': 600,
'figure.figsize': (2.5, 2),
'savefig.dpi': 600,
'xtick.direction': 'in',
'ytick.direction': 'in',
'xtick.labelsize': 5,
'ytick.labelsize': 5,
'legend.fontsize': 5,
}
plt.rcParams.update(parameters)
plt.figure()
plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵
font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 5} # 设置坐标标签的字体大小，字体
plt.scatter(list(range(result_data.shape[0])), result_data, c='black', s=0.001)
# 画出 y=1 这条水平线
plt.axhline(0.5, c='red', label='Failure threshold')
# 箭头指向上面的水平线
# plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
# alpha=0.9, overhang=0.5)
plt.text(result_data.shape[0] * 2 / 3 + 1000, 0.7, "Truth Fault", fontsize=5, color='red',
verticalalignment='top')
plt.axvline(result_data.shape[0] * 2 / 3, c='blue', ls='-.')
# plt.xticks(range(6), ('06/09/17', '12/09/17', '18/09/17', '24/09/17', '29/09/17')) # 设置x轴的标尺
plt.text(result_data.shape[0] * 4 / 5, 0.6, "Fault", fontsize=5, color='black', verticalalignment='top',
horizontalalignment='center',
bbox={'facecolor': 'grey',
'pad': 2.5}, fontdict=font1)
plt.text(result_data.shape[0] * 1 / 3, 0.4, "Norm", fontsize=5, color='black', verticalalignment='top',
horizontalalignment='center',
bbox={'facecolor': 'grey',
'pad': 2.5}, fontdict=font1)
indices = [result_data.shape[0] *i / 4 for i in range(5)]
print(indices)
classes = ['N', 'IF', 'OF','TRC', 'oo'] # for i in range(cls):
# 第一个是迭代对象，表示坐标的显示顺序，第二个参数是坐标轴显示列表
# plt.xticks(indices. classes,rotation=45)#设置横坐标方向，rotation=45为45度倾斜# plt.yticks(indices, classes, rotation=45)
plt.xticks([index + 0.5 for index in indices], classes, rotation=45) # 设置横坐标方向，rotation=45为45度倾斜
# plt.yticks([index + 0.5 for index in indices], classes, rotation=45)
plt.ylabel('Actual label', fontsize=5)
plt.xlabel('Predicted label', fontsize=5)
plt.tight_layout()
plt.show()
pass
if __name__ == '__main__':
file_name = "E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_C\RNet_C_timestamp120_feature10_result.csv"
# result_data = np.recfromcsv(file_name)
result_data = np.loadtxt(file_name, delimiter=",")
result_data = np.array(result_data)
data = np.zeros([208, ])
result_data = np.concatenate([result_data, data], axis=0)
print(result_data)
print(result_data.shape)
plot_result(result_data)