Changes:方差计算代码与测试

variance.py

@@ -0,0 +1,51 @@
+from algorithm import algorithm
+import json
+import ray
+
+@ray.remote
+class variance(algorithm):
+    def __init__(self):
+        self.config_dict_ = None
+        self.config_ = None
+        self.variance_number_ = 10
+        self.present_number_ = 0
+        self.window_1_ = []
+        self.sum = 0
+        self.average = 0
+        self.variance = 0
+        self.variance_sum = 0
+
+    def set_config(self, config):
+        self.config_ = config
+        self.config_dict_ = json.loads(self.config_)
+        self.variance_number_ = self.config_dict_["VARIANCE_NUMBER"]
+
+    def config(self):
+        return self.config_
+
+    def eval(self, value):
+        self.present_number_ = len(self.window_1_)
+        if self.present_number_ < self.variance_number_:
+            self.window_1_.append(value)
+            self.sum = 0
+            for i in self.window_1_:
+                self.sum = self.sum + i
+            self.average = self.sum / len(self.window_1_)
+            self.variance_sum = 0
+            for i in self.window_1_:
+                self.variance_sum = self.variance_sum + (i-self.average)*(i-self.average)
+            self.variance = self.variance_sum / len(self.window_1_)
+            return self.variance
+        else:
+            self.sum = 0
+            del self.window_1_[0]
+            self.window_1_.append(value)
+            for i in self.window_1_:
+                self.sum = self.sum + i
+            self.average = self.sum / len(self.window_1_)
+            self.variance_sum = 0
+            for i in self.window_1_:
+                self.variance_sum = self.variance_sum + (i-self.average)*(i-self.average)
+            self.variance = self.variance_sum / len(self.window_1_)
+            return self.variance
+

variance_test_csv.py

@@ -0,0 +1,31 @@
+import pandas as pd
+from variance import variance
+import ray
+
+ray.init()
+
+ray.RAY_DISABLE_MEMORY_MONITOR=1
+filepath = "D:/python_project_data/1.csv"
+origin_data = pd.read_csv(filepath)
+row_len = origin_data.shape[0]
+cow_len = origin_data.shape[1]
+variance_data = origin_data
+algorithm_variance = variance.remote()
+
+cow_name = "G1.TTXD1_3"
+contrast_data = pd.DataFrame()
+contrast_data[cow_name+'_origin'] = origin_data[cow_name]
+algorithm_variance.set_config.remote('{"VARIANCE_NUMBER": 5 }')
+# algorithm_step.set_config('{"CYCLE_TIME_BASE": 5 }')
+for i in range(0,row_len):
+    futures=algorithm_variance.eval.remote(origin_data.loc[i,cow_name])
+    variance_data.loc[i, cow_name]=ray.get(futures)
+
+print(variance_data.loc[:, cow_name])
+contrast_data[cow_name+'_variance'] = variance_data[cow_name]
+# average_data.to_csv("D:/python_project_data/1_disturb.csv", index=False)
+contrast_data.to_csv("D:/python_project_data/variance_data.csv", index=False)
+
+
+# 以下均为测试性能用
+# print(algorithm_step.config_)