diff --git a/Leecode/src/main/java/com/markilue/leecode/greedy/FindContentChildren.java b/Leecode/src/main/java/com/markilue/leecode/greedy/FindContentChildren.java new file mode 100644 index 0000000..b73cdb1 --- /dev/null +++ b/Leecode/src/main/java/com/markilue/leecode/greedy/FindContentChildren.java @@ -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; + + } +} diff --git a/Leecode/src/main/java/com/markilue/leecode/greedy/WiggleMaxLength.java b/Leecode/src/main/java/com/markilue/leecode/greedy/WiggleMaxLength.java new file mode 100644 index 0000000..ea143cf --- /dev/null +++ b/Leecode/src/main/java/com/markilue/leecode/greedy/WiggleMaxLength.java @@ -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); + } + + + + + + + + +} diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-3.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-3.py index 6525dc7..3ab7d3b 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-3.py +++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-3.py @@ -31,7 +31,7 @@ feature_num = 10 batch_size = 16 learning_rate = 0.001 EPOCH = 101 -model_name = "RNet_C" +model_name = "RNet_3" '''EWMA超参数''' K = 18 namuda = 0.01 @@ -48,12 +48,12 @@ 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/RNet_3/{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/RNet_3/{0}_timestamp{1}_feature{2}_max.csv".format(model_name, time_stamp, feature_num, 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) # 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.load_weights(save_name) - # # - # step_two_model = Joint_Monitoring() + step_one_model = Joint_Monitoring() + # step_one_model.load_weights(save_name) + # + step_two_model = Joint_Monitoring() # step_two_model.load_weights(save_name) #### TODO 第二步训练 @@ -678,9 +678,9 @@ if __name__ == '__main__': 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, 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_data=train_data, - # train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) + train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, + train_data=train_data, + train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) healthy_size, _, _ = train_data_healthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-34.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-34.py index e4f06f2..311cc8a 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-34.py +++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-34.py @@ -31,7 +31,7 @@ feature_num = 10 batch_size = 16 learning_rate = 0.001 EPOCH = 101 -model_name = "RNet_C" +model_name = "RNet_34" '''EWMA超参数''' K = 18 namuda = 0.01 @@ -42,18 +42,18 @@ save_name = "./model/weight/{0}/weight".format(model_name, feature_num, batch_size, 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, feature_num, batch_size, 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, 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/RNet_34/{0}_timestamp{1}_feature{2}_max.csv".format(model_name, time_stamp, feature_num, 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) # 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_two_model = Joint_Monitoring() + step_two_model = Joint_Monitoring() # step_two_model.load_weights(save_name) #### TODO 第二步训练 @@ -678,9 +678,9 @@ if __name__ == '__main__': 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, 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_data=train_data, - # train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) + train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, + train_data=train_data, + train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) healthy_size, _, _ = train_data_healthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-35.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-35.py index ca132c2..8eb8ab0 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-35.py +++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-35.py @@ -31,7 +31,7 @@ feature_num = 10 batch_size = 16 learning_rate = 0.001 EPOCH = 101 -model_name = "RNet_C" +model_name = "RNet_35" '''EWMA超参数''' K = 18 namuda = 0.01 @@ -42,18 +42,18 @@ save_name = "./model/weight/{0}/weight".format(model_name, feature_num, batch_size, 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, feature_num, batch_size, 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, 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/RNet_35/{0}_timestamp{1}_feature{2}_max.csv".format(model_name, time_stamp, feature_num, 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) # 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_two_model = Joint_Monitoring() + step_two_model = Joint_Monitoring() # step_two_model.load_weights(save_name) #### TODO 第二步训练 @@ -678,9 +678,9 @@ if __name__ == '__main__': 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, 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_data=train_data, - # train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) + train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, + train_data=train_data, + train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) healthy_size, _, _ = train_data_healthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-4.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-4.py index 924cb1b..e98323a 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-4.py +++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-4.py @@ -31,7 +31,7 @@ feature_num = 10 batch_size = 16 learning_rate = 0.001 EPOCH = 101 -model_name = "RNet_C" +model_name = "RNet_4" '''EWMA超参数''' K = 18 namuda = 0.01 @@ -42,7 +42,7 @@ save_name = "./model/weight/{0}/weight".format(model_name, feature_num, batch_size, 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, feature_num, 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) # 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_two_model = Joint_Monitoring() + step_two_model = Joint_Monitoring() # step_two_model.load_weights(save_name) #### TODO 第二步训练 @@ -678,9 +678,9 @@ if __name__ == '__main__': 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, 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_data=train_data, - # train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) + train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, + train_data=train_data, + train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) healthy_size, _, _ = train_data_healthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-45.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-45.py index dfd1cb3..599bcf8 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-45.py +++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-45.py @@ -31,7 +31,7 @@ feature_num = 10 batch_size = 16 learning_rate = 0.001 EPOCH = 101 -model_name = "RNet_C" +model_name = "RNet_45" '''EWMA超参数''' K = 18 namuda = 0.01 @@ -42,18 +42,18 @@ save_name = "./model/weight/{0}/weight".format(model_name, feature_num, batch_size, 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, feature_num, batch_size, 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, 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/RNet_45/{0}_timestamp{1}_feature{2}_max.csv".format(model_name, time_stamp, feature_num, 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) # 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_two_model = Joint_Monitoring() + step_two_model = Joint_Monitoring() # step_two_model.load_weights(save_name) #### TODO 第二步训练 @@ -678,9 +678,9 @@ if __name__ == '__main__': 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, 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_data=train_data, - # train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) + train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, + train_data=train_data, + train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) healthy_size, _, _ = train_data_healthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-5.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-5.py index 2ed6c52..9e71349 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-5.py +++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-5.py @@ -31,7 +31,7 @@ feature_num = 10 batch_size = 16 learning_rate = 0.001 EPOCH = 101 -model_name = "RNet_C" +model_name = "RNet_5" '''EWMA超参数''' K = 18 namuda = 0.01 @@ -42,7 +42,7 @@ save_name = "./model/weight/{0}/weight".format(model_name, feature_num, batch_size, 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, feature_num, 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) # 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_two_model = Joint_Monitoring() + step_two_model = Joint_Monitoring() # step_two_model.load_weights(save_name) #### TODO 第二步训练 @@ -678,9 +678,9 @@ if __name__ == '__main__': 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, 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_data=train_data, - # train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) + train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, + train_data=train_data, + train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) healthy_size, _, _ = train_data_healthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-C.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-C.py index f855bd7..e90d4ff 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-C.py +++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-C.py @@ -673,8 +673,8 @@ if __name__ == '__main__': #### TODO 第二步训练 ### healthy_data.shape: (300333,120,10) ### unhealthy_data.shape: (16594,10) - # healthy_size, _, _ = train_data_healthy.shape - # unhealthy_size, _, _ = train_data_unhealthy.shape + healthy_size, _, _ = train_data_healthy.shape + unhealthy_size, _, _ = train_data_unhealthy.shape 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_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py index f6cf365..a2766ac 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py +++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py @@ -3,19 +3,28 @@ # coding: utf-8 import matplotlib.pyplot as plt import numpy as np +import random ''' @Author : dingjiawen @Date : 2022/10/20 21:35 -@Usage : +@Usage : 测试相关画图设置 @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): parameters = { 'figure.dpi': 600, - 'figure.figsize': (2.5, 2), + 'figure.figsize': (2.8, 2), 'savefig.dpi': 600, 'xtick.direction': 'in', 'ytick.direction': 'in', @@ -27,42 +36,97 @@ def plot_result(result_data): 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) + 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 - 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 - # 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.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.text(0, 0.55, "Threshold", fontsize=5, color='red', + # 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.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', 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', + 'pad': 1.5, + 'linewidth':0.1}, fontdict=font1) + plt.text(result_data.shape[0] * 1 / 3, 0.6, "Norm", fontsize=5, color='black', verticalalignment='top', horizontalalignment='center', 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)] - print(indices) - classes = ['N', 'IF', 'OF','TRC', 'oo'] # for i in range(cls): + classes = ['01/09/17', '08/09/17', '15/09/17','22/09/17', '29/09/17'] + + 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 # 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.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 for index in indices1], classes1) + plt.ylabel('Confidence', fontsize=5) + plt.xlabel('Time', fontsize=5) plt.tight_layout() + # plt.legend(loc='best',edgecolor='black',fontsize=3) + plt.legend(loc='best',frameon=False,fontsize=3) + # plt.grid() 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" +def plot_MSE(total_MSE,total_max): + 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.loadtxt(file_name, delimiter=",") result_data = np.array(result_data) @@ -71,3 +135,24 @@ if __name__ == '__main__': print(result_data) print(result_data.shape) 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 + + + +