Differences

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--- gradient_descent [2026/03/14 09:23] – [Gradient Descent] hkimscil
+++ gradient_descent [2026/03/14 11:21] (current) – [output] hkimscil
@@ Line 92: / Line 92: @@
 summary(mo)
-plot(seq(1, length(ssrs)), ssrs)
+k <- min(ssrs)
-plot(seq(1, length(ssrs)), srs)
+j <- as[min.pos.ssrs]
+plot(seq(1, length(as)), ssrs, type="l")
+text(4500,2000000,paste("ssrs = ", k, "\n" , "is minimum value when a = ", j))
 tail(ssrs)
 max(ssrs)
@@ Line 226: / Line 229: @@
 >
-> plot(seq(1, length(ssrs)), ssrs)
+> k <- min(ssrs)
-> plot(seq(1, length(ssrs)), srs)
+> j <- as[min.pos.ssrs]
+> plot(seq(1, length(as)), ssrs, type="l")
+> text(4500,2000000,paste("ssrs = ", k, "\n" , "is minmum value when a = ", j))
+>
 > tail(ssrs)
 [1] 2267151 2267355 2267559 2267763 2267967 2268171
@@ Line 243: / Line 249: @@
 </code>
 </tabbox>
-{{pasted:20260314-092258.png}}
+{{pasted:20260314-104647.png}}
-{{pasted:20260314-092321.png}}
-{{pasted:20260314-092350.png}}
 위 방법은 dumb . . . . .
@@ Line 309: / Line 313: @@
 > # we use sum of square of error which oftentimes become big
 > msrloss <- function(predictions, y) {
-+   residuals <- (y - predictions)
++     residuals <- (y - predictions)
-+   return(mean(residuals^2))
++     return(mean(residuals^2))
 + }
 >
@@ Line 318: / Line 322: @@
 >
 > for (i in seq(from = -50, to = 50, by = 0.01)) {
-+   pred <- predict(x, i, b)
++     pred <- predict(x, i, b)
-+   res <- residuals(pred, y)
++     res <- residuals(pred, y)
-+   msr <- msrloss(pred, y)
++     msr <- msrloss(pred, y)
-+   msrs <- append(msrs, msr)
++     msrs <- append(msrs, msr)
-+   srs <- append(srs, mean(res))
++     srs <- append(srs, mean(res))
-+   as <- append(as, i)
++     as <- append(as, i)
 + }
 > length(msrs)
@@ Line 333: / Line 337: @@
 >
 > min(msrs)
-[1] 7766.679
+[1] 8735.055
 > min.pos.msrs <- which(msrs == min(msrs))
 > min.pos.msrs
-[1] 5828
+[1] 4896
 > print(as[min.pos.msrs])
-[1] 8.27
+[1] -1.05
 > summary(mo)
@@ Line 346: / Line 350: @@
 Residuals:
      Min       1Q   Median       3Q      Max
--259.314  -59.215    6.683   58.834  309.833
+-245.291  -67.967   -3.722   63.440  242.174
 Coefficients:
             Estimate Std. Error t value Pr(>|t|)
-(Intercept)    8.266     12.546   0.659    0.511
+(Intercept)   -1.047     14.289  -0.073    0.942
-x             11.888      2.433   4.887 2.11e-06 ***
+x             12.900      2.870   4.495 1.19e-05 ***
 ---
 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-Residual standard error: 88.57 on 198 degrees of freedom
+Residual standard error: 93.93 on 198 degrees of freedom
-Multiple R-squared:  0.1076,	Adjusted R-squared:  0.1031
+Multiple R-squared:  0.09258,	Adjusted R-squared:  0.088
-F-statistic: 23.88 on 1 and 198 DF,  p-value: 2.111e-06
+F-statistic:  20.2 on 1 and 198 DF,  p-value: 1.185e-05
 > plot(seq(1, length(msrs)), msrs)
 > plot(seq(1, length(srs)), srs)
 > tail(msrs)
-[1] 9504.208 9505.041 9505.875 9506.710 9507.544 9508.379
+[1] 11335.75 11336.77 11337.79 11338.81 11339.84 11340.86
 > max(msrs)
-[1] 11161.64
+[1] 11340.86
 > min(msrs)
-[1] 7766.679
+[1] 8735.055
 > tail(srs)
-[1] -41.68368 -41.69368 -41.70368 -41.71368 -41.72368 -41.73368
+[1] -50.99705 -51.00705 -51.01705 -51.02705 -51.03705 -51.04705
 > max(srs)
-[1] 58.26632
+[1] 48.95295
 > min(srs)
-[1] -41.73368
+[1] -51.04705
 >
 </code>
-{{:pasted:20250821-121009.png}}
+{{pasted:20260314-112129.png}}
-{{:pasted:20250821-121024.png}}
 ===== b값 구하기 =====