p2 data gen

p2data.py

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+import numpy as np
+import pickle
+import math
+
+DIMENTION = 3
+VAR_RANGE = 1
+N = 20
+ALPHA = 0.1
+SIGMA = VAR_RANGE * ALPHA
+
+TrainingSetFilename = "trainingset.pickle"
+TrainingSetRatio = 0.85
+DevSetFilename = "devset.pickle"
+DevSetRatio = 0.05
+TestSetFilename = "testset.pickle"
+TestSetRatio = 0.10
+AnswerFilename = "answer.pickle"
+
+def LoadAnswer():
+    with open(AnswerFilename,"rb") as fr:
+        w = pickle.load(fr)
+        b = pickle.load(fr)
+
+def SaveAnswer(w,b):
+    with open(AnswerFilename,"wb") as fw:
+        pickle.dump(w, fw)
+        pickle.dump(b, fw)
+
+def LoadData(filename):
+    with open(filename,"rb") as fr:
+        x = pickle.load(fr)
+        y = pickle.load(fr)
+    return x, y
+
+def SaveData(filename,x,y):
+    with open(filename,"wb") as fw:
+        pickle.dump(x, fw)
+        pickle.dump(y, fw)
+
+if __name__ == "__main__":
+    gen: np.random.Generator = np.random.default_rng()
+    or_weight = (gen.uniform(high = VAR_RANGE,low = -VAR_RANGE,size=(DIMENTION,1)))
+    or_bias = (gen.uniform(high = VAR_RANGE, low = -VAR_RANGE,size=()))
+
+    input_x = gen.uniform(low = -VAR_RANGE,high=VAR_RANGE,size=(N,DIMENTION))
+    y = input_x @ or_weight + or_bias
+    error = gen.normal(0,SIGMA,size = (N,1))
+    y += error
+
+    """
+    p = gen.permutation(N)
+    x,y= x[p],y[p]
+    """
+    
+    print("success to generate dataset")
+    SaveAnswer(or_weight,or_bias)
+    TrainingIndex = math.ceil(N * TrainingSetRatio)
+    TrainingSetX = input_x[0:TrainingIndex]
+    TrainingSetY = y[0:TrainingIndex]
+    SaveData(TrainingSetFilename,TrainingSetX,TrainingSetY)
+
+    DevSetIndex = math.ceil(TrainingIndex + N * DevSetRatio)
+    DevSetX = input_x[TrainingIndex:DevSetIndex]
+    DevSetY = y[TrainingIndex:DevSetIndex]
+    SaveData(DevSetFilename,DevSetX,DevSetY)
+
+    TestSetX = input_x[DevSetIndex:N]
+    TestSetY = y[DevSetIndex:N]
+    SaveData(TestSetFilename,TestSetX,TestSetY)
+    print("success to save dataset")