From d7ec888d687725d47b8789578f0bd96876b475b4 Mon Sep 17 00:00:00 2001 From: navanchauhan Date: Sun, 7 Aug 2022 22:54:35 -0400 Subject: rebuild --- docs/feed.rss | 893 +++++++++++++++++++++++++++++----------------------------- 1 file changed, 448 insertions(+), 445 deletions(-) (limited to 'docs/feed.rss') diff --git a/docs/feed.rss b/docs/feed.rss index f8cfec1..1cb2662 100644 --- a/docs/feed.rss +++ b/docs/feed.rss @@ -4,8 +4,8 @@ Navan's Archive Rare Tips, Tricks and Posts https://web.navan.dev/en - Fri, 05 Aug 2022 16:00:10 -0000 - Fri, 05 Aug 2022 16:00:10 -0000 + Sun, 07 Aug 2022 22:53:57 -0000 + Sun, 07 Aug 2022 22:53:57 -0000 250 @@ -900,7 +900,8 @@ It is possible that this additional step of mapping could be avoided by storing

Output:

-
[55786, 18374, 299592, 662622, 6054, 227458, 139687, 303950, 70000, 129307, 70823, 5766, 23950, 137696, 655723, 32842, 413269, 145994, 197990, 373832]
+
55786
+[55786, 18374, 299592, 662622, 6054, 227458, 139687, 303950, 70000, 129307, 70823, 5766, 23950, 137696, 655723, 32842, 413269, 145994, 197990, 373832]
 Now You See Me (2013): An FBI agent and an Interpol detective track a team of illusionists who pull off bank heists during their performances and reward their audiences with the money.
 Trapped (1949): U.S. Treasury Department agents go after a ring of counterfeiters.
 Brute Sanity (2018): An FBI-trained neuropsychologist teams up with a thief to find a reality-altering device while her insane ex-boss unleashes bizarre traps to stop her.
@@ -1130,25 +1131,25 @@ me.fset  me.fset3 me.iset
 
<script src="https://cdn.jsdelivr.net/gh/aframevr/aframe@1c2407b26c61958baa93967b5412487cd94b290b/dist/aframe-master.min.js"></script>
 <script src="https://raw.githack.com/AR-js-org/AR.js/master/aframe/build/aframe-ar-nft.js"></script>
 
-<style>
-  .arjs-loader {
-    height: 100%;
-    width: 100%;
-    position: absolute;
-    top: 0;
-    left: 0;
-    background-color: rgba(0, 0, 0, 0.8);
-    z-index: 9999;
-    display: flex;
-    justify-content: center;
-    align-items: center;
-  }
-
-  .arjs-loader div {
-    text-align: center;
-    font-size: 1.25em;
-    color: white;
-  }
+<style>
+  .arjs-loader {
+    height: 100%;
+    width: 100%;
+    position: absolute;
+    top: 0;
+    left: 0;
+    background-color: rgba(0, 0, 0, 0.8);
+    z-index: 9999;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+  }
+
+  .arjs-loader div {
+    text-align: center;
+    font-size: 1.25em;
+    color: white;
+  }
 </style>
 
 <body style="margin : 0px; overflow: hidden;">
@@ -1541,31 +1542,31 @@ values using the X values. We then plot it to compare the actual data and predic
 print(training_cost, coefficient1, constant)
 

 
-
Epoch 1000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 2000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 3000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 4000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 5000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 6000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 7000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 8000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 9000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 10000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 11000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 12000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 13000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 14000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 15000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 16000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 17000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 18000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 19000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 20000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 21000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 22000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 23000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 24000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
-Epoch 25000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+
Epoch 1000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 2000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 3000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 4000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 5000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 6000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 7000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 8000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 9000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 10000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 11000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 12000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 13000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 14000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 15000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 16000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 17000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 18000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 19000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 20000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 21000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 22000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 23000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 24000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
+Epoch 25000 : Training Cost: 88999125000.0  a,b: 180396.42 -478869.12
 88999125000.0 180396.42 -478869.12
 

 
@@ -1600,31 +1601,31 @@ values using the X values. We then plot it to compare the actual data and predic
 print(training_cost, coefficient1, coefficient2, constant)
 

 
-
Epoch 1000 : Training Cost: 52571360000.0  a,b,c: 1002.4456 1097.0197 1276.6921
-Epoch 2000 : Training Cost: 37798890000.0  a,b,c: 1952.4263 2130.2825 2469.7756
-Epoch 3000 : Training Cost: 26751185000.0  a,b,c: 2839.5825 3081.6118 3554.351
-Epoch 4000 : Training Cost: 19020106000.0  a,b,c: 3644.56 3922.9563 4486.3135
-Epoch 5000 : Training Cost: 14060446000.0  a,b,c: 4345.042 4621.4233 5212.693
-Epoch 6000 : Training Cost: 11201084000.0  a,b,c: 4921.1855 5148.1504 5689.0713
-Epoch 7000 : Training Cost: 9732740000.0  a,b,c: 5364.764 5493.0156 5906.754
-Epoch 8000 : Training Cost: 9050918000.0  a,b,c: 5685.4067 5673.182 5902.0728
-Epoch 9000 : Training Cost: 8750394000.0  a,b,c: 5906.9814 5724.8906 5734.746
-Epoch 10000 : Training Cost: 8613128000.0  a,b,c: 6057.3677 5687.3364 5461.167
-Epoch 11000 : Training Cost: 8540034600.0  a,b,c: 6160.547 5592.3022 5122.8633
-Epoch 12000 : Training Cost: 8490983000.0  a,b,c: 6233.9175 5462.025 4747.111
-Epoch 13000 : Training Cost: 8450816500.0  a,b,c: 6289.048 5310.7583 4350.6997
-Epoch 14000 : Training Cost: 8414082000.0  a,b,c: 6333.199 5147.394 3943.9294
-Epoch 15000 : Training Cost: 8378841600.0  a,b,c: 6370.7944 4977.1704 3532.476
-Epoch 16000 : Training Cost: 8344471000.0  a,b,c: 6404.468 4803.542 3120.2087
-Epoch 17000 : Training Cost: 8310785500.0  a,b,c: 6435.365 4628.1523 2709.1445
-Epoch 18000 : Training Cost: 8277482000.0  a,b,c: 6465.5493 4451.833 2300.2783
-Epoch 19000 : Training Cost: 8244650000.0  a,b,c: 6494.609 4274.826 1894.3738
-Epoch 20000 : Training Cost: 8212349000.0  a,b,c: 6522.8247 4098.1733 1491.9915
-Epoch 21000 : Training Cost: 8180598300.0  a,b,c: 6550.6567 3922.7405 1093.3868
-Epoch 22000 : Training Cost: 8149257700.0  a,b,c: 6578.489 3747.8362 698.53357
-Epoch 23000 : Training Cost: 8118325000.0  a,b,c: 6606.1973 3573.2742 307.3541
-Epoch 24000 : Training Cost: 8088001000.0  a,b,c: 6632.96 3399.878 -79.89219
-Epoch 25000 : Training Cost: 8058094600.0  a,b,c: 6659.793 3227.2517 -463.03156
+
Epoch 1000 : Training Cost: 52571360000.0  a,b,c: 1002.4456 1097.0197 1276.6921
+Epoch 2000 : Training Cost: 37798890000.0  a,b,c: 1952.4263 2130.2825 2469.7756
+Epoch 3000 : Training Cost: 26751185000.0  a,b,c: 2839.5825 3081.6118 3554.351
+Epoch 4000 : Training Cost: 19020106000.0  a,b,c: 3644.56 3922.9563 4486.3135
+Epoch 5000 : Training Cost: 14060446000.0  a,b,c: 4345.042 4621.4233 5212.693
+Epoch 6000 : Training Cost: 11201084000.0  a,b,c: 4921.1855 5148.1504 5689.0713
+Epoch 7000 : Training Cost: 9732740000.0  a,b,c: 5364.764 5493.0156 5906.754
+Epoch 8000 : Training Cost: 9050918000.0  a,b,c: 5685.4067 5673.182 5902.0728
+Epoch 9000 : Training Cost: 8750394000.0  a,b,c: 5906.9814 5724.8906 5734.746
+Epoch 10000 : Training Cost: 8613128000.0  a,b,c: 6057.3677 5687.3364 5461.167
+Epoch 11000 : Training Cost: 8540034600.0  a,b,c: 6160.547 5592.3022 5122.8633
+Epoch 12000 : Training Cost: 8490983000.0  a,b,c: 6233.9175 5462.025 4747.111
+Epoch 13000 : Training Cost: 8450816500.0  a,b,c: 6289.048 5310.7583 4350.6997
+Epoch 14000 : Training Cost: 8414082000.0  a,b,c: 6333.199 5147.394 3943.9294
+Epoch 15000 : Training Cost: 8378841600.0  a,b,c: 6370.7944 4977.1704 3532.476
+Epoch 16000 : Training Cost: 8344471000.0  a,b,c: 6404.468 4803.542 3120.2087
+Epoch 17000 : Training Cost: 8310785500.0  a,b,c: 6435.365 4628.1523 2709.1445
+Epoch 18000 : Training Cost: 8277482000.0  a,b,c: 6465.5493 4451.833 2300.2783
+Epoch 19000 : Training Cost: 8244650000.0  a,b,c: 6494.609 4274.826 1894.3738
+Epoch 20000 : Training Cost: 8212349000.0  a,b,c: 6522.8247 4098.1733 1491.9915
+Epoch 21000 : Training Cost: 8180598300.0  a,b,c: 6550.6567 3922.7405 1093.3868
+Epoch 22000 : Training Cost: 8149257700.0  a,b,c: 6578.489 3747.8362 698.53357
+Epoch 23000 : Training Cost: 8118325000.0  a,b,c: 6606.1973 3573.2742 307.3541
+Epoch 24000 : Training Cost: 8088001000.0  a,b,c: 6632.96 3399.878 -79.89219
+Epoch 25000 : Training Cost: 8058094600.0  a,b,c: 6659.793 3227.2517 -463.03156
 8058094600.0 6659.793 3227.2517 -463.03156
 

 
@@ -1660,31 +1661,31 @@ values using the X values. We then plot it to compare the actual data and predic
 print(training_cost, coefficient1, coefficient2, coefficient3, constant)
 

 
-
Epoch 1000 : Training Cost: 4279814000.0  a,b,c,d: 670.1527 694.4212 751.4653 903.9527
-Epoch 2000 : Training Cost: 3770950400.0  a,b,c,d: 742.6414 666.3489 636.94525 859.2088
-Epoch 3000 : Training Cost: 3717708300.0  a,b,c,d: 756.2582 569.3339 448.105 748.23956
-Epoch 4000 : Training Cost: 3667464000.0  a,b,c,d: 769.4476 474.0318 265.5761 654.75525
-Epoch 5000 : Training Cost: 3620040700.0  a,b,c,d: 782.32324 380.54272 89.39888 578.5136
-Epoch 6000 : Training Cost: 3575265800.0  a,b,c,d: 794.8898 288.83356 -80.5215 519.13654
-Epoch 7000 : Training Cost: 3532972000.0  a,b,c,d: 807.1608 198.87044 -244.31102 476.2061
-Epoch 8000 : Training Cost: 3493009200.0  a,b,c,d: 819.13513 110.64169 -402.0677 449.3291
-Epoch 9000 : Training Cost: 3455228400.0  a,b,c,d: 830.80255 24.0964 -553.92804 438.0652
-Epoch 10000 : Training Cost: 3419475500.0  a,b,c,d: 842.21594 -60.797424 -700.0123 441.983
-Epoch 11000 : Training Cost: 3385625300.0  a,b,c,d: 853.3363 -144.08699 -840.467 460.6356
-Epoch 12000 : Training Cost: 3353544700.0  a,b,c,d: 864.19135 -225.8125 -975.4196 493.57703
-Epoch 13000 : Training Cost: 3323125000.0  a,b,c,d: 874.778 -305.98932 -1104.9867 540.39465
-Epoch 14000 : Training Cost: 3294257000.0  a,b,c,d: 885.1007 -384.63474 -1229.277 600.65607
-Epoch 15000 : Training Cost: 3266820000.0  a,b,c,d: 895.18823 -461.819 -1348.4417 673.9051
-Epoch 16000 : Training Cost: 3240736000.0  a,b,c,d: 905.0128 -537.541 -1462.6171 759.7118
-Epoch 17000 : Training Cost: 3215895000.0  a,b,c,d: 914.60065 -611.8676 -1571.9058 857.6638
-Epoch 18000 : Training Cost: 3192216800.0  a,b,c,d: 923.9603 -684.8093 -1676.4642 967.30475
-Epoch 19000 : Training Cost: 3169632300.0  a,b,c,d: 933.08594 -756.3582 -1776.4275 1088.2198
-Epoch 20000 : Training Cost: 3148046300.0  a,b,c,d: 941.9928 -826.6257 -1871.9355 1219.9702
-Epoch 21000 : Training Cost: 3127394800.0  a,b,c,d: 950.67896 -895.6205 -1963.0989 1362.1665
-Epoch 22000 : Training Cost: 3107608600.0  a,b,c,d: 959.1487 -963.38116 -2050.0586 1514.4026
-Epoch 23000 : Training Cost: 3088618200.0  a,b,c,d: 967.4355 -1029.9625 -2132.961 1676.2717
-Epoch 24000 : Training Cost: 3070361300.0  a,b,c,d: 975.52875 -1095.4292 -2211.854 1847.4485
-Epoch 25000 : Training Cost: 3052791300.0  a,b,c,d: 983.4346 -1159.7922 -2286.9412 2027.4857
+
Epoch 1000 : Training Cost: 4279814000.0  a,b,c,d: 670.1527 694.4212 751.4653 903.9527
+Epoch 2000 : Training Cost: 3770950400.0  a,b,c,d: 742.6414 666.3489 636.94525 859.2088
+Epoch 3000 : Training Cost: 3717708300.0  a,b,c,d: 756.2582 569.3339 448.105 748.23956
+Epoch 4000 : Training Cost: 3667464000.0  a,b,c,d: 769.4476 474.0318 265.5761 654.75525
+Epoch 5000 : Training Cost: 3620040700.0  a,b,c,d: 782.32324 380.54272 89.39888 578.5136
+Epoch 6000 : Training Cost: 3575265800.0  a,b,c,d: 794.8898 288.83356 -80.5215 519.13654
+Epoch 7000 : Training Cost: 3532972000.0  a,b,c,d: 807.1608 198.87044 -244.31102 476.2061
+Epoch 8000 : Training Cost: 3493009200.0  a,b,c,d: 819.13513 110.64169 -402.0677 449.3291
+Epoch 9000 : Training Cost: 3455228400.0  a,b,c,d: 830.80255 24.0964 -553.92804 438.0652
+Epoch 10000 : Training Cost: 3419475500.0  a,b,c,d: 842.21594 -60.797424 -700.0123 441.983
+Epoch 11000 : Training Cost: 3385625300.0  a,b,c,d: 853.3363 -144.08699 -840.467 460.6356
+Epoch 12000 : Training Cost: 3353544700.0  a,b,c,d: 864.19135 -225.8125 -975.4196 493.57703
+Epoch 13000 : Training Cost: 3323125000.0  a,b,c,d: 874.778 -305.98932 -1104.9867 540.39465
+Epoch 14000 : Training Cost: 3294257000.0  a,b,c,d: 885.1007 -384.63474 -1229.277 600.65607
+Epoch 15000 : Training Cost: 3266820000.0  a,b,c,d: 895.18823 -461.819 -1348.4417 673.9051
+Epoch 16000 : Training Cost: 3240736000.0  a,b,c,d: 905.0128 -537.541 -1462.6171 759.7118
+Epoch 17000 : Training Cost: 3215895000.0  a,b,c,d: 914.60065 -611.8676 -1571.9058 857.6638
+Epoch 18000 : Training Cost: 3192216800.0  a,b,c,d: 923.9603 -684.8093 -1676.4642 967.30475
+Epoch 19000 : Training Cost: 3169632300.0  a,b,c,d: 933.08594 -756.3582 -1776.4275 1088.2198
+Epoch 20000 : Training Cost: 3148046300.0  a,b,c,d: 941.9928 -826.6257 -1871.9355 1219.9702
+Epoch 21000 : Training Cost: 3127394800.0  a,b,c,d: 950.67896 -895.6205 -1963.0989 1362.1665
+Epoch 22000 : Training Cost: 3107608600.0  a,b,c,d: 959.1487 -963.38116 -2050.0586 1514.4026
+Epoch 23000 : Training Cost: 3088618200.0  a,b,c,d: 967.4355 -1029.9625 -2132.961 1676.2717
+Epoch 24000 : Training Cost: 3070361300.0  a,b,c,d: 975.52875 -1095.4292 -2211.854 1847.4485
+Epoch 25000 : Training Cost: 3052791300.0  a,b,c,d: 983.4346 -1159.7922 -2286.9412 2027.4857
 3052791300.0 983.4346 -1159.7922 -2286.9412 2027.4857
 

 
@@ -1721,31 +1722,31 @@ values using the X values. We then plot it to compare the actual data and predic
 print(training_cost, coefficient1, coefficient2, coefficient3, coefficient4, constant)
 

 
-
Epoch 1000 : Training Cost: 1902632600.0  a,b,c,d: 84.48304 52.210594 54.791424 142.51952 512.0343
-Epoch 2000 : Training Cost: 1854316200.0  a,b,c,d: 88.998955 13.073557 14.276088 223.55667 1056.4655
-Epoch 3000 : Training Cost: 1812812400.0  a,b,c,d: 92.9462 -22.331177 -15.262934 327.41858 1634.9054
-Epoch 4000 : Training Cost: 1775716000.0  a,b,c,d: 96.42522 -54.64535 -35.829437 449.5028 2239.1392
-Epoch 5000 : Training Cost: 1741494100.0  a,b,c,d: 99.524734 -84.43976 -49.181057 585.85876 2862.4915
-Epoch 6000 : Training Cost: 1709199600.0  a,b,c,d: 102.31984 -112.19895 -56.808075 733.1876 3499.6199
-Epoch 7000 : Training Cost: 1678261800.0  a,b,c,d: 104.87324 -138.32709 -59.9442 888.79626 4146.2944
-Epoch 8000 : Training Cost: 1648340600.0  a,b,c,d: 107.23536 -163.15173 -59.58964 1050.524 4798.979
-Epoch 9000 : Training Cost: 1619243400.0  a,b,c,d: 109.44742 -186.9409 -56.53944 1216.6432 5454.9463
-Epoch 10000 : Training Cost: 1590821900.0  a,b,c,d: 111.54233 -209.91287 -51.423084 1385.8513 6113.5137
-Epoch 11000 : Training Cost: 1563042200.0  a,b,c,d: 113.54405 -232.21953 -44.73371 1557.1084 6771.7046
-Epoch 12000 : Training Cost: 1535855600.0  a,b,c,d: 115.471565 -253.9838 -36.851135 1729.535 7429.069
-Epoch 13000 : Training Cost: 1509255300.0  a,b,c,d: 117.33939 -275.29697 -28.0714 1902.5308 8083.9634
-Epoch 14000 : Training Cost: 1483227000.0  a,b,c,d: 119.1605 -296.2472 -18.618649 2075.6094 8735.381
-Epoch 15000 : Training Cost: 1457726700.0  a,b,c,d: 120.94584 -316.915 -8.650095 2248.3247 9384.197
-Epoch 16000 : Training Cost: 1432777300.0  a,b,c,d: 122.69806 -337.30704 1.7027153 2420.5771 10028.871
-Epoch 17000 : Training Cost: 1408365000.0  a,b,c,d: 124.42179 -357.45245 12.33499 2592.2983 10669.157
-Epoch 18000 : Training Cost: 1384480000.0  a,b,c,d: 126.12332 -377.39734 23.168756 2763.0933 11305.027
-Epoch 19000 : Training Cost: 1361116800.0  a,b,c,d: 127.80568 -397.16415 34.160156 2933.0452 11935.669
-Epoch 20000 : Training Cost: 1338288100.0  a,b,c,d: 129.4674 -416.72803 45.259155 3101.7727 12561.179
-Epoch 21000 : Training Cost: 1315959700.0  a,b,c,d: 131.11403 -436.14285 56.4436 3269.3142 13182.058
-Epoch 22000 : Training Cost: 1294164700.0  a,b,c,d: 132.74377 -455.3779 67.6757 3435.3833 13796.807
-Epoch 23000 : Training Cost: 1272863600.0  a,b,c,d: 134.35779 -474.45316 78.96117 3600.264 14406.58
-Epoch 24000 : Training Cost: 1252052600.0  a,b,c,d: 135.9583 -493.38254 90.268616 3764.0078 15010.481
-Epoch 25000 : Training Cost: 1231713700.0  a,b,c,d: 137.54753 -512.1876 101.59372 3926.4897 15609.368
+
Epoch 1000 : Training Cost: 1902632600.0  a,b,c,d: 84.48304 52.210594 54.791424 142.51952 512.0343
+Epoch 2000 : Training Cost: 1854316200.0  a,b,c,d: 88.998955 13.073557 14.276088 223.55667 1056.4655
+Epoch 3000 : Training Cost: 1812812400.0  a,b,c,d: 92.9462 -22.331177 -15.262934 327.41858 1634.9054
+Epoch 4000 : Training Cost: 1775716000.0  a,b,c,d: 96.42522 -54.64535 -35.829437 449.5028 2239.1392
+Epoch 5000 : Training Cost: 1741494100.0  a,b,c,d: 99.524734 -84.43976 -49.181057 585.85876 2862.4915
+Epoch 6000 : Training Cost: 1709199600.0  a,b,c,d: 102.31984 -112.19895 -56.808075 733.1876 3499.6199
+Epoch 7000 : Training Cost: 1678261800.0  a,b,c,d: 104.87324 -138.32709 -59.9442 888.79626 4146.2944
+Epoch 8000 : Training Cost: 1648340600.0  a,b,c,d: 107.23536 -163.15173 -59.58964 1050.524 4798.979
+Epoch 9000 : Training Cost: 1619243400.0  a,b,c,d: 109.44742 -186.9409 -56.53944 1216.6432 5454.9463
+Epoch 10000 : Training Cost: 1590821900.0  a,b,c,d: 111.54233 -209.91287 -51.423084 1385.8513 6113.5137
+Epoch 11000 : Training Cost: 1563042200.0  a,b,c,d: 113.54405 -232.21953 -44.73371 1557.1084 6771.7046
+Epoch 12000 : Training Cost: 1535855600.0  a,b,c,d: 115.471565 -253.9838 -36.851135 1729.535 7429.069
+Epoch 13000 : Training Cost: 1509255300.0  a,b,c,d: 117.33939 -275.29697 -28.0714 1902.5308 8083.9634
+Epoch 14000 : Training Cost: 1483227000.0  a,b,c,d: 119.1605 -296.2472 -18.618649 2075.6094 8735.381
+Epoch 15000 : Training Cost: 1457726700.0  a,b,c,d: 120.94584 -316.915 -8.650095 2248.3247 9384.197
+Epoch 16000 : Training Cost: 1432777300.0  a,b,c,d: 122.69806 -337.30704 1.7027153 2420.5771 10028.871
+Epoch 17000 : Training Cost: 1408365000.0  a,b,c,d: 124.42179 -357.45245 12.33499 2592.2983 10669.157
+Epoch 18000 : Training Cost: 1384480000.0  a,b,c,d: 126.12332 -377.39734 23.168756 2763.0933 11305.027
+Epoch 19000 : Training Cost: 1361116800.0  a,b,c,d: 127.80568 -397.16415 34.160156 2933.0452 11935.669
+Epoch 20000 : Training Cost: 1338288100.0  a,b,c,d: 129.4674 -416.72803 45.259155 3101.7727 12561.179
+Epoch 21000 : Training Cost: 1315959700.0  a,b,c,d: 131.11403 -436.14285 56.4436 3269.3142 13182.058
+Epoch 22000 : Training Cost: 1294164700.0  a,b,c,d: 132.74377 -455.3779 67.6757 3435.3833 13796.807
+Epoch 23000 : Training Cost: 1272863600.0  a,b,c,d: 134.35779 -474.45316 78.96117 3600.264 14406.58
+Epoch 24000 : Training Cost: 1252052600.0  a,b,c,d: 135.9583 -493.38254 90.268616 3764.0078 15010.481
+Epoch 25000 : Training Cost: 1231713700.0  a,b,c,d: 137.54753 -512.1876 101.59372 3926.4897 15609.368
 1231713700.0 137.54753 -512.1876 101.59372 3926.4897 15609.368
 

 
@@ -1781,31 +1782,31 @@ values using the X values. We then plot it to compare the actual data and predic
         constant = sess.run(f)
 

 
-
Epoch 1000 : Training Cost: 1409200100.0  a,b,c,d,e,f: 7.949472 7.46219 55.626034 184.29028 484.00223 1024.0083
-Epoch 2000 : Training Cost: 1306882400.0  a,b,c,d,e,f: 8.732181 -4.0085897 73.25298 315.90103 904.08887 2004.9749
-Epoch 3000 : Training Cost: 1212606000.0  a,b,c,d,e,f: 9.732249 -16.90125 86.28379 437.06552 1305.055 2966.2188
-Epoch 4000 : Training Cost: 1123640400.0  a,b,c,d,e,f: 10.74851 -29.82692 98.59997 555.331 1698.4631 3917.9155
-Epoch 5000 : Training Cost: 1039694300.0  a,b,c,d,e,f: 11.75426 -42.598194 110.698326 671.64355 2085.5513 4860.8535
-Epoch 6000 : Training Cost: 960663550.0  a,b,c,d,e,f: 12.745439 -55.18337 122.644936 786.00214 2466.1638 5794.3735
-Epoch 7000 : Training Cost: 886438340.0  a,b,c,d,e,f: 13.721028 -67.57168 134.43822 898.3691 2839.9958 6717.659
-Epoch 8000 : Training Cost: 816913100.0  a,b,c,d,e,f: 14.679965 -79.75113 146.07385 1008.66895 3206.6692 7629.812
-Epoch 9000 : Training Cost: 751971500.0  a,b,c,d,e,f: 15.62181 -91.71608 157.55713 1116.7715 3565.8323 8529.976
-Epoch 10000 : Training Cost: 691508740.0  a,b,c,d,e,f: 16.545347 -103.4531 168.88321 1222.6348 3916.9785 9416.236
-Epoch 11000 : Training Cost: 635382000.0  a,b,c,d,e,f: 17.450052 -114.954254 180.03932 1326.1565 4259.842 10287.99
-Epoch 12000 : Training Cost: 583477250.0  a,b,c,d,e,f: 18.334944 -126.20821 191.02948 1427.2095 4593.8 11143.449
-Epoch 13000 : Training Cost: 535640400.0  a,b,c,d,e,f: 19.198917 -137.20206 201.84718 1525.6926 4918.5327 11981.633
-Epoch 14000 : Training Cost: 491722240.0  a,b,c,d,e,f: 20.041153 -147.92719 212.49709 1621.5496 5233.627 12800.468
-Epoch 15000 : Training Cost: 451559520.0  a,b,c,d,e,f: 20.860966 -158.37456 222.97133 1714.7141 5538.676 13598.337
-Epoch 16000 : Training Cost: 414988960.0  a,b,c,d,e,f: 21.657421 -168.53406 233.27422 1805.0874 5833.1978 14373.658
-Epoch 17000 : Training Cost: 381837920.0  a,b,c,d,e,f: 22.429693 -178.39536 243.39914 1892.5883 6116.847 15124.394
-Epoch 18000 : Training Cost: 351931300.0  a,b,c,d,e,f: 23.176882 -187.94789 253.3445 1977.137 6389.117 15848.417
-Epoch 19000 : Training Cost: 325074400.0  a,b,c,d,e,f: 23.898485 -197.18741 263.12512 2058.6716 6649.8037 16543.95
-Epoch 20000 : Training Cost: 301073570.0  a,b,c,d,e,f: 24.593851 -206.10497 272.72385 2137.1797 6898.544 17209.367
-Epoch 21000 : Training Cost: 279727000.0  a,b,c,d,e,f: 25.262104 -214.69217 282.14642 2212.6372 7135.217 17842.854
-Epoch 22000 : Training Cost: 260845550.0  a,b,c,d,e,f: 25.903376 -222.94969 291.4003 2284.9844 7359.4644 18442.408
-Epoch 23000 : Training Cost: 244218030.0  a,b,c,d,e,f: 26.517094 -230.8697 300.45532 2354.3003 7571.261 19007.49
-Epoch 24000 : Training Cost: 229660080.0  a,b,c,d,e,f: 27.102589 -238.44817 309.35342 2420.4185 7770.5728 19536.19
-Epoch 25000 : Training Cost: 216972400.0  a,b,c,d,e,f: 27.660324 -245.69016 318.10062 2483.3608 7957.354 20027.707
+
Epoch 1000 : Training Cost: 1409200100.0  a,b,c,d,e,f: 7.949472 7.46219 55.626034 184.29028 484.00223 1024.0083
+Epoch 2000 : Training Cost: 1306882400.0  a,b,c,d,e,f: 8.732181 -4.0085897 73.25298 315.90103 904.08887 2004.9749
+Epoch 3000 : Training Cost: 1212606000.0  a,b,c,d,e,f: 9.732249 -16.90125 86.28379 437.06552 1305.055 2966.2188
+Epoch 4000 : Training Cost: 1123640400.0  a,b,c,d,e,f: 10.74851 -29.82692 98.59997 555.331 1698.4631 3917.9155
+Epoch 5000 : Training Cost: 1039694300.0  a,b,c,d,e,f: 11.75426 -42.598194 110.698326 671.64355 2085.5513 4860.8535
+Epoch 6000 : Training Cost: 960663550.0  a,b,c,d,e,f: 12.745439 -55.18337 122.644936 786.00214 2466.1638 5794.3735
+Epoch 7000 : Training Cost: 886438340.0  a,b,c,d,e,f: 13.721028 -67.57168 134.43822 898.3691 2839.9958 6717.659
+Epoch 8000 : Training Cost: 816913100.0  a,b,c,d,e,f: 14.679965 -79.75113 146.07385 1008.66895 3206.6692 7629.812
+Epoch 9000 : Training Cost: 751971500.0  a,b,c,d,e,f: 15.62181 -91.71608 157.55713 1116.7715 3565.8323 8529.976
+Epoch 10000 : Training Cost: 691508740.0  a,b,c,d,e,f: 16.545347 -103.4531 168.88321 1222.6348 3916.9785 9416.236
+Epoch 11000 : Training Cost: 635382000.0  a,b,c,d,e,f: 17.450052 -114.954254 180.03932 1326.1565 4259.842 10287.99
+Epoch 12000 : Training Cost: 583477250.0  a,b,c,d,e,f: 18.334944 -126.20821 191.02948 1427.2095 4593.8 11143.449
+Epoch 13000 : Training Cost: 535640400.0  a,b,c,d,e,f: 19.198917 -137.20206 201.84718 1525.6926 4918.5327 11981.633
+Epoch 14000 : Training Cost: 491722240.0  a,b,c,d,e,f: 20.041153 -147.92719 212.49709 1621.5496 5233.627 12800.468
+Epoch 15000 : Training Cost: 451559520.0  a,b,c,d,e,f: 20.860966 -158.37456 222.97133 1714.7141 5538.676 13598.337
+Epoch 16000 : Training Cost: 414988960.0  a,b,c,d,e,f: 21.657421 -168.53406 233.27422 1805.0874 5833.1978 14373.658
+Epoch 17000 : Training Cost: 381837920.0  a,b,c,d,e,f: 22.429693 -178.39536 243.39914 1892.5883 6116.847 15124.394
+Epoch 18000 : Training Cost: 351931300.0  a,b,c,d,e,f: 23.176882 -187.94789 253.3445 1977.137 6389.117 15848.417
+Epoch 19000 : Training Cost: 325074400.0  a,b,c,d,e,f: 23.898485 -197.18741 263.12512 2058.6716 6649.8037 16543.95
+Epoch 20000 : Training Cost: 301073570.0  a,b,c,d,e,f: 24.593851 -206.10497 272.72385 2137.1797 6898.544 17209.367
+Epoch 21000 : Training Cost: 279727000.0  a,b,c,d,e,f: 25.262104 -214.69217 282.14642 2212.6372 7135.217 17842.854
+Epoch 22000 : Training Cost: 260845550.0  a,b,c,d,e,f: 25.903376 -222.94969 291.4003 2284.9844 7359.4644 18442.408
+Epoch 23000 : Training Cost: 244218030.0  a,b,c,d,e,f: 26.517094 -230.8697 300.45532 2354.3003 7571.261 19007.49
+Epoch 24000 : Training Cost: 229660080.0  a,b,c,d,e,f: 27.102589 -238.44817 309.35342 2420.4185 7770.5728 19536.19
+Epoch 25000 : Training Cost: 216972400.0  a,b,c,d,e,f: 27.660324 -245.69016 318.10062 2483.3608 7957.354 20027.707
 216972400.0 27.660324 -245.69016 318.10062 2483.3608 7957.354 20027.707
 

 
@@ -2681,20 +2682,20 @@ I created a sample JSON with only 3 examples (I know, very less, but works for a
 
 
Screenshot of Sample Dataset

 
-
[
-    {
-        "tokens": ["Tell","me","about","the","drug","Aspirin","."],
-        "labels": ["NONE","NONE","NONE","NONE","NONE","COMPOUND","NONE"]
-    },
-    {
-        "tokens": ["Please","tell","me","information","about","the","compound","salicylic","acid","."],
-        "labels": ["NONE","NONE","NONE","NONE","NONE","NONE","NONE","COMPOUND","COMPOUND","NONE"]
-    },
-    {
-        "tokens": ["Information","about","the","compound","Ibuprofen","please","."],
-        "labels": ["NONE","NONE","NONE","NONE","COMPOUND","NONE","NONE"]
-    }
-]
+
[
+    {
+        "tokens": ["Tell","me","about","the","drug","Aspirin","."],
+        "labels": ["NONE","NONE","NONE","NONE","NONE","COMPOUND","NONE"]
+    },
+    {
+        "tokens": ["Please","tell","me","information","about","the","compound","salicylic","acid","."],
+        "labels": ["NONE","NONE","NONE","NONE","NONE","NONE","NONE","COMPOUND","COMPOUND","NONE"]
+    },
+    {
+        "tokens": ["Information","about","the","compound","Ibuprofen","please","."],
+        "labels": ["NONE","NONE","NONE","NONE","COMPOUND","NONE","NONE"]
+    }
+]
 

 
 
Screenshot of Create ML Text Classifier

@@ -2909,72 +2910,74 @@ Otherwise, it calls the custom action.

         rdkit_version=None,
         add_python_path=True,
         force=False):
-    """install rdkit from miniconda
-    ```
-    import rdkit_installer
-    rdkit_installer.install()
-    ```
-    """
-
-    python_path = os.path.join(
-        conda_path,
-        "lib",
-        "python{0}.{1}".format(*sys.version_info),
-        "site-packages",
-    )
-
-    if add_python_path and python_path not in sys.path:
-        logger.info("add {} to PYTHONPATH".format(python_path))
-        sys.path.append(python_path)
-
-    if os.path.isdir(os.path.join(python_path, "rdkit")):
-        logger.info("rdkit is already installed")
-        if not force:
-            return
-
-        logger.info("force re-install")
-
-    url = url_base + file_name
-    python_version = "{0}.{1}.{2}".format(*sys.version_info)
-
-    logger.info("python version: {}".format(python_version))
-
-    if os.path.isdir(conda_path):
-        logger.warning("remove current miniconda")
-        shutil.rmtree(conda_path)
-    elif os.path.isfile(conda_path):
-        logger.warning("remove {}".format(conda_path))
-        os.remove(conda_path)
-
-    logger.info('fetching installer from {}'.format(url))
-    res = requests.get(url, stream=True)
-    res.raise_for_status()
-    with open(file_name, 'wb') as f:
-        for chunk in res.iter_content(chunk_size):
-            f.write(chunk)
-    logger.info('done')
-
-    logger.info('installing miniconda to {}'.format(conda_path))
-    subprocess.check_call(["bash", file_name, "-b", "-p", conda_path])
-    logger.info('done')
-
-    logger.info("installing rdkit")
-    subprocess.check_call([
-        os.path.join(conda_path, "bin", "conda"),
-        "install",
-        "--yes",
-        "-c", "rdkit",
-        "python=={}".format(python_version),
-        "rdkit" if rdkit_version is None else "rdkit=={}".format(rdkit_version)])
-    logger.info("done")
-
-    import rdkit
-    logger.info("rdkit-{} installation finished!".format(rdkit.__version__))
-
-
-if __name__ == "__main__":
-    install()
+    """install rdkit from miniconda
+   
 

+
+
import rdkit_installer
+rdkit_installer.install()
+```
+"""
+
+python_path = os.path.join(
+    conda_path,
+    "lib",
+    "python{0}.{1}".format(*sys.version_info),
+    "site-packages",
+)
+
+if add_python_path and python_path not in sys.path:
+    logger.info("add {} to PYTHONPATH".format(python_path))
+    sys.path.append(python_path)
+
+if os.path.isdir(os.path.join(python_path, "rdkit")):
+    logger.info("rdkit is already installed")
+    if not force:
+        return
+
+    logger.info("force re-install")
+
+url = url_base + file_name
+python_version = "{0}.{1}.{2}".format(*sys.version_info)
+
+logger.info("python version: {}".format(python_version))
+
+if os.path.isdir(conda_path):
+    logger.warning("remove current miniconda")
+    shutil.rmtree(conda_path)
+elif os.path.isfile(conda_path):
+    logger.warning("remove {}".format(conda_path))
+    os.remove(conda_path)
+
+logger.info('fetching installer from {}'.format(url))
+res = requests.get(url, stream=True)
+res.raise_for_status()
+with open(file_name, 'wb') as f:
+    for chunk in res.iter_content(chunk_size):
+        f.write(chunk)
+logger.info('done')
+
+logger.info('installing miniconda to {}'.format(conda_path))
+subprocess.check_call(["bash", file_name, "-b", "-p", conda_path])
+logger.info('done')
+
+logger.info("installing rdkit")
+subprocess.check_call([
+    os.path.join(conda_path, "bin", "conda"),
+    "install",
+    "--yes",
+    "-c", "rdkit",
+    "python=={}".format(python_version),
+    "rdkit" if rdkit_version is None else "rdkit=={}".format(rdkit_version)])
+logger.info("done")
+
+import rdkit
+logger.info("rdkit-{} installation finished!".format(rdkit.__version__))
+

+
+
if name == "main":
+    install()
+```

 ]]>
 		
 		
@@ -3100,7 +3103,7 @@ a CUDA compatible MXNet package.

 
 
Downloading the Dataset

 
-
!wget -q "https://github.com/joolsa/fake_real_news_dataset/raw/master/fake_or_real_news.csv.zip"
+
!wget -q "https://github.com/joolsa/fake_real_news_dataset/raw/master/fake_or_real_news.csv.zip"
 !unzip fake_or_real_news.csv.zip
 

 
@@ -3343,176 +3346,176 @@ DescriptionThe bag-of-words model is a simplifying representation used in NLP, i
 </main>
 
 <script src="https://gitcdn.xyz/repo/rbren/rss-parser/master/dist/rss-parser.js"></script>
-<script>
-
-const feeds = {
-    "BuzzFeed - India": {
-      "link":"https://www.buzzfeed.com/in.xml",
-      "summary":true
-    },
-    "New Yorker": {
-      "link":"http://www.newyorker.com/feed/news",
-    },
-    "Vox":{
-      "link":"https://www.vox.com/rss/index.xml",
-      "limit": 3
-    },
-    "r/Jokes":{
-      "link":"https://reddit.com/r/Jokes/hot/.rss?sort=hot",
-      "ignore": ["repost","discord"]
-    }
-}
-
-const config_extra = {
-"Responsive-Images": true,
-"direct-link": false,
-"show-date":false,
-"left-column":false,
-"defaults": {
-  "limit": 5,
-  "summary": true
-}
-}
-
-const CORS_PROXY = "https://cors-anywhere.herokuapp.com/"
-
-var contents_title = document.createElement("h2")
-contents_title.textContent = "Contents"
-contents_title.classList.add("pb-1")
-document.getElementById("contents").appendChild(contents_title)
-
-async function myfunc(key){
-
-  var count_lim = feeds[key]["limit"]
-  var count_lim = (count_lim === undefined) ? config_extra["defaults"]["limit"] : count_lim
-
-  var show_summary = feeds[key]["summary"]
-  var show_summary = (show_summary === undefined) ? config_extra["defaults"]["summary"] : show_summary
-
-  var ignore_tags = feeds[key]["ignore"]
-  var ignore_tags = (ignore_tags === undefined) ? [] : ignore_tags
-
-  var contents = document.createElement("a")
-  contents.href = "#" + key
-  contents.classList.add("list-group-item","list-group-item-action")
-  contents.textContent = key
-  document.getElementById("contents").appendChild(contents)
-  var feed_div = document.createElement("div")
-  feed_div.id = key
-  feed_div.setAttribute("id", key);
-  var title = document.createElement("h2");
-  title.textContent = "From " + key;
-  title.classList.add("pb-1")
-  feed_div.appendChild(title)
-  document.getElementById("feed").appendChild(feed_div)
-  var parser = new RSSParser();
-  var countPosts = 0
-  parser.parseURL(CORS_PROXY + feeds[key]["link"], function(err, feed) {
-    if (err) throw err;
-    feed.items.forEach(function(entry) {
-      if (countPosts < count_lim) {
-
-      var skip = false
-      for(var i = 0; i < ignore_tags.length; i++) {
-        if (entry.title.includes(ignore_tags[i])){
-          var skip = true
-        } else if (entry.content.includes(ignore_tags[i])){
-          var skip = true
-        }
-      }
-
-      if (!skip) {
-
-      var node = document.createElement("div");
-      node.classList.add("card","mb-3");
-      var row = document.createElement("div")
-      row.classList.add("row","no-gutters")
-
-      if (config_extra["left-column"]){
-      var left_col = document.createElement("div")
-      left_col.classList.add("col-md-2")
-      var left_col_body = document.createElement("div")
-      left_col_body.classList.add("card-body")
-      }
-
-      var right_col = document.createElement("div")
-      if (config_extra["left-column"]){
-        right_col.classList.add("col-md-10")
-      }
-      var node_title = document.createElement("h5")
-
-      node_title.classList.add("card-header")
-      node_title.innerHTML = entry.title
-
-      node_body = document.createElement("div")
-      node_body.classList.add("card-body")
-
-      node_content = document.createElement("p")
-
-      if (show_summary){
-        node_content.innerHTML = entry.content
-      }
-      node_content.classList.add("card-text")
-
-      if (config_extra["direct-link"]){
-      node_link = document.createElement("p")
-      node_link.classList.add("card-text")
-      node_link.innerHTML = "<b>Link:</b> <a href='" + entry.link +"'>Direct Link</a>"
-      if (config_extra["left-column"]){
-      left_col_body.appendChild(node_link)
-        } else {
-          node_content.appendChild(node_link)
-        }
-      }
-
-      if (config_extra["show-date"]){
-        node_date = document.createElement("p")
-        node_date.classList.add("card-text")
-        node_date.innerHTML = "<p><b>Date: </b>" + entry.pubDate + "</p>"
-        if (config_extra["left-column"]){
-        left_col_body.appendChild(node_date)
-          } else {
-            node_content.appendChild(node_date)
-
-        }
-      }
-
-      node.appendChild(node_title)
-
-      node_body.appendChild(node_content)
-
-      right_col.appendChild(node_body)
-
-      if (config_extra["left-column"]){
-        left_col.appendChild(left_col_body)
-        row.appendChild(left_col)
-      }
-
-      row.appendChild(right_col)
-
-      node.appendChild(row)
-
-      document.getElementById(key).appendChild(node)
-      countPosts+=1
-    }
-    }
-  })
-
-  if (config_extra["Responsive-Images"]){
-  var inputs = document.getElementsByTagName('img')
-      for(var i = 0; i < inputs.length; i++) {
-        inputs[i].classList.add("img-fluid")
-      }
-  }
-
-  })
-
-  return true
-}
-(async () => {
-for(var key in feeds) {
-  let result = await myfunc(key);
-}})();
+<script>
+
+const feeds = {
+    "BuzzFeed - India": {
+      "link":"https://www.buzzfeed.com/in.xml",
+      "summary":true
+    },
+    "New Yorker": {
+      "link":"http://www.newyorker.com/feed/news",
+    },
+    "Vox":{
+      "link":"https://www.vox.com/rss/index.xml",
+      "limit": 3
+    },
+    "r/Jokes":{
+      "link":"https://reddit.com/r/Jokes/hot/.rss?sort=hot",
+      "ignore": ["repost","discord"]
+    }
+}
+
+const config_extra = {
+"Responsive-Images": true,
+"direct-link": false,
+"show-date":false,
+"left-column":false,
+"defaults": {
+  "limit": 5,
+  "summary": true
+}
+}
+
+const CORS_PROXY = "https://cors-anywhere.herokuapp.com/"
+
+var contents_title = document.createElement("h2")
+contents_title.textContent = "Contents"
+contents_title.classList.add("pb-1")
+document.getElementById("contents").appendChild(contents_title)
+
+async function myfunc(key){
+
+  var count_lim = feeds[key]["limit"]
+  var count_lim = (count_lim === undefined) ? config_extra["defaults"]["limit"] : count_lim
+
+  var show_summary = feeds[key]["summary"]
+  var show_summary = (show_summary === undefined) ? config_extra["defaults"]["summary"] : show_summary
+
+  var ignore_tags = feeds[key]["ignore"]
+  var ignore_tags = (ignore_tags === undefined) ? [] : ignore_tags
+
+  var contents = document.createElement("a")
+  contents.href = "#" + key
+  contents.classList.add("list-group-item","list-group-item-action")
+  contents.textContent = key
+  document.getElementById("contents").appendChild(contents)
+  var feed_div = document.createElement("div")
+  feed_div.id = key
+  feed_div.setAttribute("id", key);
+  var title = document.createElement("h2");
+  title.textContent = "From " + key;
+  title.classList.add("pb-1")
+  feed_div.appendChild(title)
+  document.getElementById("feed").appendChild(feed_div)
+  var parser = new RSSParser();
+  var countPosts = 0
+  parser.parseURL(CORS_PROXY + feeds[key]["link"], function(err, feed) {
+    if (err) throw err;
+    feed.items.forEach(function(entry) {
+      if (countPosts < count_lim) {
+
+      var skip = false
+      for(var i = 0; i < ignore_tags.length; i++) {
+        if (entry.title.includes(ignore_tags[i])){
+          var skip = true
+        } else if (entry.content.includes(ignore_tags[i])){
+          var skip = true
+        }
+      }
+
+      if (!skip) {
+
+      var node = document.createElement("div");
+      node.classList.add("card","mb-3");
+      var row = document.createElement("div")
+      row.classList.add("row","no-gutters")
+
+      if (config_extra["left-column"]){
+      var left_col = document.createElement("div")
+      left_col.classList.add("col-md-2")
+      var left_col_body = document.createElement("div")
+      left_col_body.classList.add("card-body")
+      }
+
+      var right_col = document.createElement("div")
+      if (config_extra["left-column"]){
+        right_col.classList.add("col-md-10")
+      }
+      var node_title = document.createElement("h5")
+
+      node_title.classList.add("card-header")
+      node_title.innerHTML = entry.title
+
+      node_body = document.createElement("div")
+      node_body.classList.add("card-body")
+
+      node_content = document.createElement("p")
+
+      if (show_summary){
+        node_content.innerHTML = entry.content
+      }
+      node_content.classList.add("card-text")
+
+      if (config_extra["direct-link"]){
+      node_link = document.createElement("p")
+      node_link.classList.add("card-text")
+      node_link.innerHTML = "<b>Link:</b> <a href='" + entry.link +"'>Direct Link</a>"
+      if (config_extra["left-column"]){
+      left_col_body.appendChild(node_link)
+        } else {
+          node_content.appendChild(node_link)
+        }
+      }
+
+      if (config_extra["show-date"]){
+        node_date = document.createElement("p")
+        node_date.classList.add("card-text")
+        node_date.innerHTML = "<p><b>Date: </b>" + entry.pubDate + "</p>"
+        if (config_extra["left-column"]){
+        left_col_body.appendChild(node_date)
+          } else {
+            node_content.appendChild(node_date)
+
+        }
+      }
+
+      node.appendChild(node_title)
+
+      node_body.appendChild(node_content)
+
+      right_col.appendChild(node_body)
+
+      if (config_extra["left-column"]){
+        left_col.appendChild(left_col_body)
+        row.appendChild(left_col)
+      }
+
+      row.appendChild(right_col)
+
+      node.appendChild(row)
+
+      document.getElementById(key).appendChild(node)
+      countPosts+=1
+    }
+    }
+  })
+
+  if (config_extra["Responsive-Images"]){
+  var inputs = document.getElementsByTagName('img')
+      for(var i = 0; i < inputs.length; i++) {
+        inputs[i].classList.add("img-fluid")
+      }
+  }
+
+  })
+
+  return true
+}
+(async () => {
+for(var key in feeds) {
+  let result = await myfunc(key);
+}})();
 
 </script>
 <noscript>Uh Oh! Your browser does not support JavaScript or JavaScript is currently disabled. Please enable JavaScript or switch to a different browser.</noscript>
@@ -3858,39 +3861,39 @@ return path(str, boost::filesystem::native);
 
 
\

 
-
+-------------------------+------------------------+
-|           path          |         image          |
-+-------------------------+------------------------+
-|  ./train/default/1.jpg  | Height: 224 Width: 224 |
-|  ./train/default/10.jpg | Height: 224 Width: 224 |
-| ./train/default/100.jpg | Height: 224 Width: 224 |
-| ./train/default/101.jpg | Height: 224 Width: 224 |
-| ./train/default/102.jpg | Height: 224 Width: 224 |
-| ./train/default/103.jpg | Height: 224 Width: 224 |
-| ./train/default/104.jpg | Height: 224 Width: 224 |
-| ./train/default/105.jpg | Height: 224 Width: 224 |
-| ./train/default/106.jpg | Height: 224 Width: 224 |
-| ./train/default/107.jpg | Height: 224 Width: 224 |
-+-------------------------+------------------------+
+
+-------------------------+------------------------+
+|           path          |         image          |
++-------------------------+------------------------+
+|  ./train/default/1.jpg  | Height: 224 Width: 224 |
+|  ./train/default/10.jpg | Height: 224 Width: 224 |
+| ./train/default/100.jpg | Height: 224 Width: 224 |
+| ./train/default/101.jpg | Height: 224 Width: 224 |
+| ./train/default/102.jpg | Height: 224 Width: 224 |
+| ./train/default/103.jpg | Height: 224 Width: 224 |
+| ./train/default/104.jpg | Height: 224 Width: 224 |
+| ./train/default/105.jpg | Height: 224 Width: 224 |
+| ./train/default/106.jpg | Height: 224 Width: 224 |
+| ./train/default/107.jpg | Height: 224 Width: 224 |
++-------------------------+------------------------+
 [2028 rows x 2 columns]
-Note: Only the head of the SFrame is printed.
+Note: Only the head of the SFrame is printed.
 You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
 +-------------------------+------------------------+---------+
-|           path          |         image          |  label  |
-+-------------------------+------------------------+---------+
-|  ./train/default/1.jpg  | Height: 224 Width: 224 | default |
-|  ./train/default/10.jpg | Height: 224 Width: 224 | default |
-| ./train/default/100.jpg | Height: 224 Width: 224 | default |
-| ./train/default/101.jpg | Height: 224 Width: 224 | default |
-| ./train/default/102.jpg | Height: 224 Width: 224 | default |
-| ./train/default/103.jpg | Height: 224 Width: 224 | default |
-| ./train/default/104.jpg | Height: 224 Width: 224 | default |
-| ./train/default/105.jpg | Height: 224 Width: 224 | default |
-| ./train/default/106.jpg | Height: 224 Width: 224 | default |
-| ./train/default/107.jpg | Height: 224 Width: 224 | default |
-+-------------------------+------------------------+---------+
+|           path          |         image          |  label  |
++-------------------------+------------------------+---------+
+|  ./train/default/1.jpg  | Height: 224 Width: 224 | default |
+|  ./train/default/10.jpg | Height: 224 Width: 224 | default |
+| ./train/default/100.jpg | Height: 224 Width: 224 | default |
+| ./train/default/101.jpg | Height: 224 Width: 224 | default |
+| ./train/default/102.jpg | Height: 224 Width: 224 | default |
+| ./train/default/103.jpg | Height: 224 Width: 224 | default |
+| ./train/default/104.jpg | Height: 224 Width: 224 | default |
+| ./train/default/105.jpg | Height: 224 Width: 224 | default |
+| ./train/default/106.jpg | Height: 224 Width: 224 | default |
+| ./train/default/107.jpg | Height: 224 Width: 224 | default |
++-------------------------+------------------------+---------+
 [2028 rows x 3 columns]
-Note: Only the head of the SFrame is printed.
+Note: Only the head of the SFrame is printed.
 You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
 

 
@@ -3950,28 +3953,28 @@ return path(str, boost::filesystem::native);
 Completed 1536/1633
 Completed 1600/1633
 Completed 1633/1633
-PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
-          You can set ``validation_set=None`` to disable validation tracking.
-
-Logistic regression:
---------------------------------------------------------
-Number of examples          : 1551
-Number of classes           : 3
-Number of feature columns   : 1
-Number of unpacked features : 2048
-Number of coefficients      : 4098
+PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
+          You can set ``validation_set=None`` to disable validation tracking.
+
+Logistic regression:
+--------------------------------------------------------
+Number of examples          : 1551
+Number of classes           : 3
+Number of feature columns   : 1
+Number of unpacked features : 2048
+Number of coefficients      : 4098
 Starting L-BFGS
 --------------------------------------------------------
 +-----------+----------+-----------+--------------+-------------------+---------------------+
-| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
-+-----------+----------+-----------+--------------+-------------------+---------------------+
-| 0         | 6        | 0.018611  | 0.891830     | 0.553836          | 0.560976            |
-| 1         | 10       | 0.390832  | 1.622383     | 0.744681          | 0.792683            |
-| 2         | 11       | 0.488541  | 1.943987     | 0.733075          | 0.804878            |
-| 3         | 14       | 2.442703  | 2.512545     | 0.727917          | 0.841463            |
-| 4         | 15       | 2.442703  | 2.826964     | 0.861380          | 0.853659            |
-| 9         | 28       | 2.340435  | 5.492035     | 0.941328          | 0.975610            |
-+-----------+----------+-----------+--------------+-------------------+---------------------+
+| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |
++-----------+----------+-----------+--------------+-------------------+---------------------+
+| 0         | 6        | 0.018611  | 0.891830     | 0.553836          | 0.560976            |
+| 1         | 10       | 0.390832  | 1.622383     | 0.744681          | 0.792683            |
+| 2         | 11       | 0.488541  | 1.943987     | 0.733075          | 0.804878            |
+| 3         | 14       | 2.442703  | 2.512545     | 0.727917          | 0.841463            |
+| 4         | 15       | 2.442703  | 2.826964     | 0.861380          | 0.853659            |
+| 9         | 28       | 2.340435  | 5.492035     | 0.941328          | 0.975610            |
++-----------+----------+-----------+--------------+-------------------+---------------------+
 Performing feature extraction on resized images...
 Completed  64/395
 Completed 128/395
-- 
cgit v1.2.3