質問編集履歴
1
誤字訂正
test
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File without changes
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test
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@@ -24,7 +24,68 @@
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yy = xx[:, 0] ~ xx[:, 1]
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return deta1
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27
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+
# 重みの学習
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28
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def weightlearning(wwold, errork, xxk, eta):
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wwnew = wwold + eta*errork*xxk
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return wwnew
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# 線形結合器
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def linearcombiner(ww, xxk):
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y = np.dot(ww,xxk)
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return y
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# 平均誤り
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40
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def checkerrorrate(error, shiftlen, k):
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if(k>shiftlen):
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errorshift = np.abs(error[k+1-shiftlen:k])
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else:
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errorshift = np.abs(error[0:k])
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errorave = np.average(errorshift)
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return errorave
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# ステップ関数
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def stepfunction(x):
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if x>=0:
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return 1
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else:
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return 0
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# メイン関数
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def main():
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58
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eta = 5.0e-1
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59
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epsilon = 0.001
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60
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shiftlen = 100
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# データを読み込む．
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-
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62
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notgatedata = deta1
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63
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xx = notgatedata["x"]
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kk, nn = xx.shape
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one = np.ones([kk,1])
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xx = np.concatenate((one, xx), 1)
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kk, nn = xx.shape
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68
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zztrue = notgatedata["y"]
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print("zztrue size=", zztrue.shape)
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# 繰返し：学習過程
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wwold = [0.0, 0.0, 0.0]
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error = np.zeros(kk)
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errorave = np.zeros(kk)
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ww = np.empty([kk,nn])
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for k in range(kk):
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yyk = linearcombiner(wwold, xx[k])
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zzk = stepfunction(yyk)
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error[k] = zztrue[k] - zzk
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errorave[k] = checkerrorrate(error, shiftlen, k)
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print("k={0} zztrue={1:.4f} zz={2:.4f} errorave={3:.8f}".format(k, zztrue[k], zzk, errorave[k]))
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if(k>shiftlen and errorave[k]<epsilon):
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break
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wwnew = weightlearning(wwold, error[k], xx[k], eta)
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wwold = wwnew
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ww[k,:] = wwold
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# 重みの学習結果を表示
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print("重みの学習結果: w0=", wwold[0], "w1=", wwold[1], "w2=", wwold[2])
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88
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```
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90
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