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leecode更新

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markilue 2022-10-24 11:53:43 +08:00
parent 10c949ab6c
commit 88a7aefa97
10 changed files with 433 additions and 74 deletions
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71
Leecode/src/main/java/com/markilue/leecode/greedy/FindContentChildren.java Normal file
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@ -0,0 +1,71 @@
package com.markilue.leecode.greedy;
import org.junit.Test;
import java.util.Arrays;
import java.util.Comparator;
/**
* @BelongsProject: Leecode
* @BelongsPackage: com.markilue.leecode.greedy
* @Author: markilue
* @CreateTime: 2022-10-24 09:34
* @Description:
* TODO 力扣455题 分发饼干:
* 假设你是一位很棒的家长，想要给你的孩子们一些小饼干。但是，每个孩子最多只能给一块饼干。
* 对每个孩子 i，都有一个胃口值 g[i]，这是能让孩子们满足胃口的饼干的最小尺寸；
* 并且每块饼干 j，都有一个尺寸 s[j] 。如果 s[j] >= g[i]，我们可以将这个饼干 j 分配给孩子 i ，这个孩子会得到满足。
* 你的目标是尽可能满足越多数量的孩子，并输出这个最大数值。
*
* @Version: 1.0
*/
public class FindContentChildren {
@Test
public void test(){
int[] g= {1,2,3};
int[] s={1,1};
System.out.println(findContentChildren(g,s));
}
@Test
public void test1(){
int[] g= {1,2};
int[] s={1,2,3};
System.out.println(findContentChildren(g,s));
}
/**
* 取饼干最大的取满足孩子最大的，如果满足不了，就一直找到满足的孩子为止，如果找不到了就直接返回；；饼干找完了也返回
* 速度击败10.72%，内存击败5.17%
* @param g
* @param s
* @return
*/
public int findContentChildren(int[] g, int[] s) {
Arrays.sort(g);
Arrays.sort(s);
int result = 0;
int j=s.length-1;
for (int i = g.length-1; i >= 0; i--) {
if (j==-1) {
return result;
}
if(s[j]>=g[i]){
j--;
result++;
}
}
return result;
}
}

203
Leecode/src/main/java/com/markilue/leecode/greedy/WiggleMaxLength.java Normal file
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@ -0,0 +1,203 @@
package com.markilue.leecode.greedy;
import org.junit.Test;
/**
* @BelongsProject: Leecode
* @BelongsPackage: com.markilue.leecode.greedy
* @Author: markilue
* @CreateTime: 2022-10-24 10:26
* @Description: TODO 力扣376题 摆动序列：
* 如果连续数字之间的差严格地在正数和负数之间交替，则数字序列称为 摆动序列 。第一个差（如果存在的话）可能是正数或负数。仅有一个元素或者含两个不等元素的序列也视作摆动序列。
* 例如， [1, 7, 4, 9, 2, 5] 是一个 摆动序列 ，因为差值 (6, -3, 5, -7, 3) 是正负交替出现的。
* 相反，[1, 4, 7, 2, 5] 和 [1, 7, 4, 5, 5] 不是摆动序列，第一个序列是因为它的前两个差值都是正数，第二个序列是因为它的最后一个差值为零。
* 子序列 可以通过从原始序列中删除一些（也可以不删除）元素来获得，剩下的元素保持其原始顺序。
* 给你一个整数数组 nums ，返回 nums 中作为 摆动序列 的 最长子序列的长度
* @Version: 1.0
*/
public class WiggleMaxLength {
@Test
public void test() {
int[] nums = {1, 7, 4, 9, 2, 5};
System.out.println(wiggleMaxLength1(nums));
}
@Test
public void test1() {
int[] nums = {1, 17, 5, 10, 13, 15, 10, 5, 16, 8};
System.out.println(wiggleMaxLength2(nums));
}
@Test
public void test2() {
int[] nums = {1, 2, 3, 4, 5, 6, 7, 8, 9};
System.out.println(wiggleMaxLength2(nums));
}
@Test
public void test3() {
int[] nums = {9, 8, 7, 6, 5, 4, 3, 2, 1};
System.out.println(wiggleMaxLength1(nums));
}
public int wiggleMaxLength(int[] nums) {
int result = 0;
boolean flag = true; //true就找比现在大的
for (int i = 0; i < nums.length; i++) {
//找比当前大的
if (flag && i > 0 && nums[i] > nums[i - 1]) {
continue;
}
//找比当前小的
if (!flag && i > 0 && nums[i] < nums[i - 1]) {
continue;
}
//无论是从哪个出来的,找到了一个峰或者谷
result++;
//如果是找大的出来的
if (flag) {
//找到一个比他小的
flag = false;
} else {
flag = true;
}
}
flag = false; //true就找比现在大的
int result1 = 0;
for (int i = 0; i < nums.length; i++) {
//找比当前大的
if (flag && i > 0 && nums[i] > nums[i - 1]) {
continue;
}
//找比当前小的
if (!flag && i > 0 && nums[i] < nums[i - 1]) {
continue;
}
//无论是从哪个出来的,找到了一个峰或者谷
result1++;
//如果是找大的出来的
if (flag) {
//找到一个比他小的
flag = false;
} else {
flag = true;
}
}
return result > result1 ? result : result1;
}
/**
* 代码随想录方法
* 速度击败100%，内存击败54.73%
* @param nums
* @return
*/
public int wiggleMaxLength1(int[] nums) {
int result = 1;
int curdiff = 0; //记录现在的差值
int prediff = 0; //记录上次的差值
for (int i = 0; i < nums.length-1; i++) {
curdiff = nums[i + 1] - nums[i];
if ((prediff >= 0 && curdiff < 0) || (prediff <= 0 && curdiff > 0)) {
result++;
prediff = curdiff;
}
}
return result;
}
/**
* 官方动态规划法：
* 注意到每有一个「峰」到「谷」的下降趋势，down 值才会增加，每有一个「谷」到「峰」的上升趋势，up 值才会增加。
* 且过程中 down 与 up 的差的绝对值值恒不大于 1，即 up≤down+1 且down≤up+1，
* 于是有max(up,down+1)=down+1 且 max(up+1,down)=up+1。这样我们可以省去不必要的比较大小的过程。
* @return
*/
public int wiggleMaxLength2(int[] nums) {
int n = nums.length;
if (n < 2) {
return n;
}
int[] up = new int[n]; //记录上升摆动序列的最大长度
int[] down = new int[n]; //记录下降摆动序列的最大长度
up[0] = down[0] = 1;
for (int i = 1; i < n; i++) {
if (nums[i] > nums[i - 1]) {
//如果当前数比之前的数大，那么下降摆动序列就遇上了峰值，这时候就记录上升序列和下降序列+1谁大
up[i] = Math.max(up[i - 1], down[i - 1] + 1);
down[i] = down[i - 1];
} else if (nums[i] < nums[i - 1]) {
up[i] = up[i - 1];
down[i] = Math.max(up[i - 1] + 1, down[i - 1]);
} else {
up[i] = up[i - 1];
down[i] = down[i - 1];
}
}
return Math.max(up[n - 1], down[n - 1]);
}
/**
* 官方优化的动态规划法：注意到方法一中，我们仅需要前一个状态来进行转移，所以我们维护两个变量即可。
* @param nums
* @return
*/
public int wiggleMaxLength3(int[] nums) {
int n = nums.length;
if (n < 2) {
return n;
}
int up = 1, down = 1;
for (int i = 1; i < n; i++) {
if (nums[i] > nums[i - 1]) {
up = Math.max(up, down + 1);
} else if (nums[i] < nums[i - 1]) {
down = Math.max(up + 1, down);
}
}
return Math.max(up, down);
}
}

20
TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-3.py
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@ -31,7 +31,7 @@ feature_num = 10
batch_size = 16 batch_size = 16
learning_rate = 0.001 learning_rate = 0.001
EPOCH = 101 EPOCH = 101
model_name = "RNet_C" model_name = "RNet_3"
'''EWMA超参数''' '''EWMA超参数'''
K = 18 K = 18
namuda = 0.01 namuda = 0.01
@ -48,12 +48,12 @@ save_step_two_name = "./model/two_weight/{0}_weight_epoch6_99899_9996/weight".fo
batch_size, batch_size,
EPOCH) EPOCH)
save_mse_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_result.csv".format(model_name, save_mse_name = "./mse/RNet_3/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
EPOCH) EPOCH)
save_max_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_max.csv".format(model_name, save_max_name = "./mse/RNet_3/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
@ -662,10 +662,10 @@ if __name__ == '__main__':
# train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy) # train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy)
# 导入第一步已经训练好的模型,一个继续训练，一个只输出结果 # 导入第一步已经训练好的模型,一个继续训练，一个只输出结果
# step_one_model = Joint_Monitoring() step_one_model = Joint_Monitoring()
# # step_one_model.load_weights(save_name) # step_one_model.load_weights(save_name)
# # #
# step_two_model = Joint_Monitoring() step_two_model = Joint_Monitoring()
# step_two_model.load_weights(save_name) # step_two_model.load_weights(save_name)
#### TODO 第二步训练 #### TODO 第二步训练
@ -678,9 +678,9 @@ if __name__ == '__main__':
healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy, healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy,
unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy) unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy)
# train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, train_step_two(step_one_model=step_one_model, step_two_model=step_two_model,
# train_data=train_data, train_data=train_data,
# train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1))
healthy_size, _, _ = train_data_healthy.shape healthy_size, _, _ = train_data_healthy.shape
unhealthy_size, _, _ = train_data_unhealthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape

18
TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-34.py
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@ -31,7 +31,7 @@ feature_num = 10
batch_size = 16 batch_size = 16
learning_rate = 0.001 learning_rate = 0.001
EPOCH = 101 EPOCH = 101
model_name = "RNet_C" model_name = "RNet_34"
'''EWMA超参数''' '''EWMA超参数'''
K = 18 K = 18
namuda = 0.01 namuda = 0.01
@ -42,18 +42,18 @@ save_name = "./model/weight/{0}/weight".format(model_name,
feature_num, feature_num,
batch_size, batch_size,
EPOCH) EPOCH)
save_step_two_name = "./model/two_weight/{0}_weight_epoch6_99899_9996/weight".format(model_name, save_step_two_name = "./model/two_weight/{0}_weight/weight".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
EPOCH) EPOCH)
save_mse_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_result.csv".format(model_name, save_mse_name = "./mse/RNet_34/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
EPOCH) EPOCH)
save_max_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_max.csv".format(model_name, save_max_name = "./mse/RNet_34/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
@ -662,10 +662,10 @@ if __name__ == '__main__':
# train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy) # train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy)
# 导入第一步已经训练好的模型,一个继续训练，一个只输出结果 # 导入第一步已经训练好的模型,一个继续训练，一个只输出结果
# step_one_model = Joint_Monitoring() step_one_model = Joint_Monitoring()
# # step_one_model.load_weights(save_name) # # step_one_model.load_weights(save_name)
# # # #
# step_two_model = Joint_Monitoring() step_two_model = Joint_Monitoring()
# step_two_model.load_weights(save_name) # step_two_model.load_weights(save_name)
#### TODO 第二步训练 #### TODO 第二步训练
@ -678,9 +678,9 @@ if __name__ == '__main__':
healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy, healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy,
unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy) unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy)
# train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, train_step_two(step_one_model=step_one_model, step_two_model=step_two_model,
# train_data=train_data, train_data=train_data,
# train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1))
healthy_size, _, _ = train_data_healthy.shape healthy_size, _, _ = train_data_healthy.shape
unhealthy_size, _, _ = train_data_unhealthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape

18
TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-35.py
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@ -31,7 +31,7 @@ feature_num = 10
batch_size = 16 batch_size = 16
learning_rate = 0.001 learning_rate = 0.001
EPOCH = 101 EPOCH = 101
model_name = "RNet_C" model_name = "RNet_35"
'''EWMA超参数''' '''EWMA超参数'''
K = 18 K = 18
namuda = 0.01 namuda = 0.01
@ -42,18 +42,18 @@ save_name = "./model/weight/{0}/weight".format(model_name,
feature_num, feature_num,
batch_size, batch_size,
EPOCH) EPOCH)
save_step_two_name = "./model/two_weight/{0}_weight_epoch6_99899_9996/weight".format(model_name, save_step_two_name = "./model/two_weight/{0}_weight/weight".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
EPOCH) EPOCH)
save_mse_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_result.csv".format(model_name, save_mse_name = "./mse/RNet_35/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
EPOCH) EPOCH)
save_max_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_max.csv".format(model_name, save_max_name = "./mse/RNet_35/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
@ -662,10 +662,10 @@ if __name__ == '__main__':
# train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy) # train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy)
# 导入第一步已经训练好的模型,一个继续训练，一个只输出结果 # 导入第一步已经训练好的模型,一个继续训练，一个只输出结果
# step_one_model = Joint_Monitoring() step_one_model = Joint_Monitoring()
# # step_one_model.load_weights(save_name) # # step_one_model.load_weights(save_name)
# # # #
# step_two_model = Joint_Monitoring() step_two_model = Joint_Monitoring()
# step_two_model.load_weights(save_name) # step_two_model.load_weights(save_name)
#### TODO 第二步训练 #### TODO 第二步训练
@ -678,9 +678,9 @@ if __name__ == '__main__':
healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy, healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy,
unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy) unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy)
# train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, train_step_two(step_one_model=step_one_model, step_two_model=step_two_model,
# train_data=train_data, train_data=train_data,
# train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1))
healthy_size, _, _ = train_data_healthy.shape healthy_size, _, _ = train_data_healthy.shape
unhealthy_size, _, _ = train_data_unhealthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape

14
TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-4.py
View File

@ -31,7 +31,7 @@ feature_num = 10
batch_size = 16 batch_size = 16
learning_rate = 0.001 learning_rate = 0.001
EPOCH = 101 EPOCH = 101
model_name = "RNet_C" model_name = "RNet_4"
'''EWMA超参数''' '''EWMA超参数'''
K = 18 K = 18
namuda = 0.01 namuda = 0.01
@ -42,7 +42,7 @@ save_name = "./model/weight/{0}/weight".format(model_name,
feature_num, feature_num,
batch_size, batch_size,
EPOCH) EPOCH)
save_step_two_name = "./model/two_weight/{0}_weight_epoch6_99899_9996/weight".format(model_name, save_step_two_name = "./model/two_weight/{0}_weight/weight".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
@ -662,10 +662,10 @@ if __name__ == '__main__':
# train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy) # train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy)
# 导入第一步已经训练好的模型,一个继续训练，一个只输出结果 # 导入第一步已经训练好的模型,一个继续训练，一个只输出结果
# step_one_model = Joint_Monitoring() step_one_model = Joint_Monitoring()
# # step_one_model.load_weights(save_name) # # step_one_model.load_weights(save_name)
# # # #
# step_two_model = Joint_Monitoring() step_two_model = Joint_Monitoring()
# step_two_model.load_weights(save_name) # step_two_model.load_weights(save_name)
#### TODO 第二步训练 #### TODO 第二步训练
@ -678,9 +678,9 @@ if __name__ == '__main__':
healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy, healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy,
unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy) unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy)
# train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, train_step_two(step_one_model=step_one_model, step_two_model=step_two_model,
# train_data=train_data, train_data=train_data,
# train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1))
healthy_size, _, _ = train_data_healthy.shape healthy_size, _, _ = train_data_healthy.shape
unhealthy_size, _, _ = train_data_unhealthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape

18
TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-45.py
View File

@ -31,7 +31,7 @@ feature_num = 10
batch_size = 16 batch_size = 16
learning_rate = 0.001 learning_rate = 0.001
EPOCH = 101 EPOCH = 101
model_name = "RNet_C" model_name = "RNet_45"
'''EWMA超参数''' '''EWMA超参数'''
K = 18 K = 18
namuda = 0.01 namuda = 0.01
@ -42,18 +42,18 @@ save_name = "./model/weight/{0}/weight".format(model_name,
feature_num, feature_num,
batch_size, batch_size,
EPOCH) EPOCH)
save_step_two_name = "./model/two_weight/{0}_weight_epoch6_99899_9996/weight".format(model_name, save_step_two_name = "./model/two_weight/{0}_weight/weight".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
EPOCH) EPOCH)
save_mse_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_result.csv".format(model_name, save_mse_name = "./mse/RNet_45/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
EPOCH) EPOCH)
save_max_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_max.csv".format(model_name, save_max_name = "./mse/RNet_45/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
@ -662,10 +662,10 @@ if __name__ == '__main__':
# train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy) # train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy)
# 导入第一步已经训练好的模型,一个继续训练，一个只输出结果 # 导入第一步已经训练好的模型,一个继续训练，一个只输出结果
# step_one_model = Joint_Monitoring() step_one_model = Joint_Monitoring()
# # step_one_model.load_weights(save_name) # # step_one_model.load_weights(save_name)
# # # #
# step_two_model = Joint_Monitoring() step_two_model = Joint_Monitoring()
# step_two_model.load_weights(save_name) # step_two_model.load_weights(save_name)
#### TODO 第二步训练 #### TODO 第二步训练
@ -678,9 +678,9 @@ if __name__ == '__main__':
healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy, healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy,
unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy) unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy)
# train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, train_step_two(step_one_model=step_one_model, step_two_model=step_two_model,
# train_data=train_data, train_data=train_data,
# train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1))
healthy_size, _, _ = train_data_healthy.shape healthy_size, _, _ = train_data_healthy.shape
unhealthy_size, _, _ = train_data_unhealthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape

14
TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-5.py
View File

@ -31,7 +31,7 @@ feature_num = 10
batch_size = 16 batch_size = 16
learning_rate = 0.001 learning_rate = 0.001
EPOCH = 101 EPOCH = 101
model_name = "RNet_C" model_name = "RNet_5"
'''EWMA超参数''' '''EWMA超参数'''
K = 18 K = 18
namuda = 0.01 namuda = 0.01
@ -42,7 +42,7 @@ save_name = "./model/weight/{0}/weight".format(model_name,
feature_num, feature_num,
batch_size, batch_size,
EPOCH) EPOCH)
save_step_two_name = "./model/two_weight/{0}_weight_epoch6_99899_9996/weight".format(model_name, save_step_two_name = "./model/two_weight/{0}_weight/weight".format(model_name,
time_stamp, time_stamp,
feature_num, feature_num,
batch_size, batch_size,
@ -662,10 +662,10 @@ if __name__ == '__main__':
# train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy) # train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy)
# 导入第一步已经训练好的模型,一个继续训练，一个只输出结果 # 导入第一步已经训练好的模型,一个继续训练，一个只输出结果
# step_one_model = Joint_Monitoring() step_one_model = Joint_Monitoring()
# # step_one_model.load_weights(save_name) # # step_one_model.load_weights(save_name)
# # # #
# step_two_model = Joint_Monitoring() step_two_model = Joint_Monitoring()
# step_two_model.load_weights(save_name) # step_two_model.load_weights(save_name)
#### TODO 第二步训练 #### TODO 第二步训练
@ -678,9 +678,9 @@ if __name__ == '__main__':
healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy, healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy,
unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy) unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy)
# train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, train_step_two(step_one_model=step_one_model, step_two_model=step_two_model,
# train_data=train_data, train_data=train_data,
# train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1))
healthy_size, _, _ = train_data_healthy.shape healthy_size, _, _ = train_data_healthy.shape
unhealthy_size, _, _ = train_data_unhealthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape

4
TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-C.py
View File

@ -673,8 +673,8 @@ if __name__ == '__main__':
#### TODO 第二步训练 #### TODO 第二步训练
### healthy_data.shape: (300333,120,10) ### healthy_data.shape: (300333,120,10)
### unhealthy_data.shape: (16594,10) ### unhealthy_data.shape: (16594,10)
# healthy_size, _, _ = train_data_healthy.shape healthy_size, _, _ = train_data_healthy.shape
# unhealthy_size, _, _ = train_data_unhealthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape
train_data, train_label1, train_label2, test_data, test_label1, test_label2 = split_test_data( train_data, train_label1, train_label2, test_data, test_label1, test_label2 = split_test_data(
healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :, :], healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :, :],
healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],

127
TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py
View File

@ -3,19 +3,28 @@
# coding: utf-8 # coding: utf-8
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
import random
''' '''
@Author : dingjiawen @Author : dingjiawen
@Date : 2022/10/20 21:35 @Date : 2022/10/20 21:35
@Usage : @Usage : 测试相关画图设置
@Desc : @Desc :
''' '''
result_file_name = "E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_C\RNet_C_timestamp120_feature10_result.csv"
mse_file_name="E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_D\RNet_D_timestamp120_feature10_mse_predict1.csv"
max_file_name="E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_D\RNet_D_timestamp120_feature10_max_predict1.csv"
def plot_result(result_data): def plot_result(result_data):
parameters = { parameters = {
'figure.dpi': 600, 'figure.dpi': 600,
'figure.figsize': (2.5, 2), 'figure.figsize': (2.8, 2),
'savefig.dpi': 600, 'savefig.dpi': 600,
'xtick.direction': 'in', 'xtick.direction': 'in',
'ytick.direction': 'in', 'ytick.direction': 'in',
@ -27,42 +36,97 @@ def plot_result(result_data):
plt.figure() plt.figure()
plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵 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 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) plt.scatter(list(range(result_data.shape[0])), result_data, c='black', s=0.5,label="predict")
# 画出 y=1 这条水平线 # 画出 y=1 这条水平线
plt.axhline(0.5, c='red', label='Failure threshold') plt.axhline(0.5, c='red', label='Failure threshold',lw=1)
# 箭头指向上面的水平线 # 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', # plt.arrow(result_data.shape[0]*2/3, 0.55, 2000, 0.085, width=0.00001, ec='red',length_includes_head=True)
# alpha=0.9, overhang=0.5) # plt.text(result_data.shape[0] * 2 / 3 + 1000, 0.7, "real fault", fontsize=5, color='red',
plt.text(result_data.shape[0] * 2 / 3 + 1000, 0.7, "Truth Fault", fontsize=5, color='red', # verticalalignment='top')
verticalalignment='top') # plt.text(0, 0.55, "Threshold", fontsize=5, color='red',
plt.axvline(result_data.shape[0] * 2 / 3, c='blue', ls='-.') # verticalalignment='top')
plt.axvline(result_data.shape[0] * 2 / 3, c='blue', ls='-.',lw=1,label='real fault')
# 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.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', plt.text(result_data.shape[0] * 5 / 6, 0.4, "Fault", fontsize=5, color='black', verticalalignment='top',
horizontalalignment='center', horizontalalignment='center',
bbox={'facecolor': 'grey', bbox={'facecolor': 'grey',
'pad': 2.5}, fontdict=font1) 'pad': 1.5,
plt.text(result_data.shape[0] * 1 / 3, 0.4, "Norm", fontsize=5, color='black', verticalalignment='top', 'linewidth':0.1}, fontdict=font1)
plt.text(result_data.shape[0] * 1 / 3, 0.6, "Norm", fontsize=5, color='black', verticalalignment='top',
horizontalalignment='center', horizontalalignment='center',
bbox={'facecolor': 'grey', bbox={'facecolor': 'grey',
'pad': 2.5}, fontdict=font1) 'pad': 1.5,'linewidth':0.1}, fontdict=font1)
indices = [result_data.shape[0] *i / 4 for i in range(5)] indices = [result_data.shape[0] *i / 4 for i in range(5)]
print(indices) classes = ['01/09/17', '08/09/17', '15/09/17','22/09/17', '29/09/17']
classes = ['N', 'IF', 'OF','TRC', 'oo'] # for i in range(cls):
indices1 = [ i / 4 for i in range(5)]
classes1 = ['0', '0.25', 'Threshold','0.75', '1']
# 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 # 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(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.xticks([index + 0.5 for index in indices], classes, rotation=25) # 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.yticks([index for index in indices1], classes1)
plt.ylabel('Actual label', fontsize=5) plt.ylabel('Confidence', fontsize=5)
plt.xlabel('Predicted label', fontsize=5) plt.xlabel('Time', fontsize=5)
plt.tight_layout() plt.tight_layout()
# plt.legend(loc='best',edgecolor='black',fontsize=3)
plt.legend(loc='best',frameon=False,fontsize=3)
# plt.grid()
plt.show() plt.show()
pass pass
if __name__ == '__main__': def plot_MSE(total_MSE,total_max):
file_name = "E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_C\RNet_C_timestamp120_feature10_result.csv" parameters = {
'figure.dpi': 600,
'figure.figsize': (2.7, 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
result_data=total_MSE
# 画出 y=1 这条水平线
# plt.axhline(0.5, c='red', label='Failure threshold',lw=1)
# 箭头指向上面的水平线
# plt.arrow(result_data.shape[0]*2/3, 0.55, 2000, 0.085, width=0.00001, ec='red',length_includes_head=True)
# plt.text(result_data.shape[0] * 2 / 3 + 1000, 0.7, "real fault", fontsize=5, color='red',
# verticalalignment='top')
plt.axvline(result_data.shape[0] * 2 / 3, c='blue', ls='-.',lw=0.5,label="real fault")
indices = [result_data.shape[0] * i / 4 for i in range(5)]
classes = ['01/09/17', '08/09/17', '15/09/17', '22/09/17', '29/09/17']
plt.xticks([index + 0.5 for index in indices], classes, rotation=25) # 设置横坐标方向，rotation=45为45度倾斜
plt.ylabel('Mse', fontsize=5)
plt.xlabel('Time', fontsize=5)
plt.tight_layout()
plt.plot(total_max,"--",label="max",linewidth=0.5)
plt.plot(total_MSE,label="mse",linewidth=0.5,color='purple')
plt.legend(loc='best',frameon=False,fontsize=5)
# plt.plot(total_mean)
# plt.plot(min)
plt.show()
pass
def test_result(file_name:str=result_file_name):
# result_data = np.recfromcsv(file_name) # result_data = np.recfromcsv(file_name)
result_data = np.loadtxt(file_name, delimiter=",") result_data = np.loadtxt(file_name, delimiter=",")
result_data = np.array(result_data) result_data = np.array(result_data)
@ -71,3 +135,24 @@ if __name__ == '__main__':
print(result_data) print(result_data)
print(result_data.shape) print(result_data.shape)
plot_result(result_data) plot_result(result_data)
def test_mse(mse_file_name:str=mse_file_name,max_file_name:str=max_file_name):
mse_data = np.loadtxt(mse_file_name, delimiter=",")
max_data = np.loadtxt(max_file_name,delimiter=',')
mse_data = np.array(mse_data)
max_data = np.array(max_data)
print(mse_data.shape)
print(max_data.shape)
plot_MSE(mse_data,max_data)
if __name__ == '__main__':
# test_mse()
test_result()
pass