Predict similar scheme #680

Closed
Closed
Predict similar scheme#680

Description

@AdamShakhabov
AdamShakhabov
opened on Aug 15, 2018

Sorry for my English.

Dataset

There is dataset which contains files which describe scheme:

sample #1.txt

3103686, 2590304, 2022230, 838696
5530360, 1916721, 2022230, 430823
3103686, 3807071, 2022230, 430823
5705725, 4022485, 2022230, 975943
8043677, 3697167, 2022230, 430823
8043677, 2761756, 2022230, 430823

sample #2.txt

2994926, 3072910, 2022230, 1752477
7396944, 3072911, 2022230, 1752476
2994926, 1981531, 5573177, 558310

Each row is rectangle element (on scheme) feature vector (x, y, width, height).

Data to predict

I need train a model which can predict for such input data

input.txt

3313321, 3259181, 2022230, 558310
7039277, 3454335, 2022230, 558310
5253403, 4207799, 2022231, 558310
4073770, 2445894, 2022230, 558310
6569923, 2445894, 2022230, 558310

similar scheme.

For example, in the above example for input.txt prediction would be quite if model say that sample #1 most similar for input scheme.

Question

Which algorithm from ML.NET should I use to solve my task? Of cause I do not expect complete solution, just put me right way.
I have a little bit sub-questions to clarify my problem:

  • How preparing dataset to train: by feature describe or matrix?

  • Before some classifier should I clustering data?

Metadata

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Assignees

No one assigned

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      , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
       blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

      Skip to content
      Predict similar scheme #680
      Closed
      Closed
      Predict similar scheme#680
      Description
      @AdamShakhabov
      AdamShakhabov
      opened on Aug 15, 2018
      Sorry for my English.
      Dataset
      There is dataset which contains files which describe scheme:
      sample #1.txt
      3103686, 2590304, 2022230, 838696
5530360, 1916721, 2022230, 430823
3103686, 3807071, 2022230, 430823
5705725, 4022485, 2022230, 975943
8043677, 3697167, 2022230, 430823
8043677, 2761756, 2022230, 430823

      sample #2.txt
      2994926, 3072910, 2022230, 1752477
7396944, 3072911, 2022230, 1752476
2994926, 1981531, 5573177, 558310

      Each row is rectangle element (on scheme) feature vector (x, y, width, height).
      Data to predict
      I need train a model which can predict for such input data
      input.txt
      3313321, 3259181, 2022230, 558310
7039277, 3454335, 2022230, 558310
5253403, 4207799, 2022231, 558310
4073770, 2445894, 2022230, 558310
6569923, 2445894, 2022230, 558310

      similar scheme.
      For example, in the above example for input.txt prediction would be quite if model say that sample #1 most similar for input scheme.
      Question
      Which algorithm from ML.NET should I use to solve my task? Of cause I do not expect complete solution, just put me right way.
      
I have a little bit sub-questions to clarify my problem:
      • How preparing dataset to train: by feature describe or matrix?
      • Before some classifier should I clustering data?
      Metadata
      Metadata
      Assignees
      No one assigned
        Labels
        No labels
        No labels
        Type
        No type
        Projects
        No projects
          Milestone
          No milestone
          Relationships
          None yet
          Development
          No branches or pull requests
          Issue actions
          
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

          Skip to content
          Predict similar scheme #680
          Closed
          Closed
          Predict similar scheme#680
          Description
          @AdamShakhabov
          AdamShakhabov
          opened on Aug 15, 2018
          Sorry for my English.
          Dataset
          There is dataset which contains files which describe scheme:
          sample #1.txt
          3103686, 2590304, 2022230, 838696
5530360, 1916721, 2022230, 430823
3103686, 3807071, 2022230, 430823
5705725, 4022485, 2022230, 975943
8043677, 3697167, 2022230, 430823
8043677, 2761756, 2022230, 430823

          sample #2.txt
          2994926, 3072910, 2022230, 1752477
7396944, 3072911, 2022230, 1752476
2994926, 1981531, 5573177, 558310

          Each row is rectangle element (on scheme) feature vector (x, y, width, height).
          Data to predict
          I need train a model which can predict for such input data
          input.txt
          3313321, 3259181, 2022230, 558310
7039277, 3454335, 2022230, 558310
5253403, 4207799, 2022231, 558310
4073770, 2445894, 2022230, 558310
6569923, 2445894, 2022230, 558310

          similar scheme.
          For example, in the above example for input.txt prediction would be quite if model say that sample #1 most similar for input scheme.
          Question
          Which algorithm from ML.NET should I use to solve my task? Of cause I do not expect complete solution, just put me right way.
          
I have a little bit sub-questions to clarify my problem:
          • How preparing dataset to train: by feature describe or matrix?
          • Before some classifier should I clustering data?
          Metadata
          Metadata
          Assignees
          No one assigned
            Labels
            No labels
            No labels
            Type
            No type
            Projects
            No projects
              Milestone
              No milestone
              Relationships
              None yet
              Development
              No branches or pull requests
              Issue actions
              
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

              Skip to content
              Predict similar scheme #680
              Closed
              Closed
              Predict similar scheme#680
              Description
              @AdamShakhabov
              AdamShakhabov
              opened on Aug 15, 2018
              Sorry for my English.
              Dataset
              There is dataset which contains files which describe scheme:
              sample #1.txt
              3103686, 2590304, 2022230, 838696
5530360, 1916721, 2022230, 430823
3103686, 3807071, 2022230, 430823
5705725, 4022485, 2022230, 975943
8043677, 3697167, 2022230, 430823
8043677, 2761756, 2022230, 430823

              sample #2.txt
              2994926, 3072910, 2022230, 1752477
7396944, 3072911, 2022230, 1752476
2994926, 1981531, 5573177, 558310

              Each row is rectangle element (on scheme) feature vector (x, y, width, height).
              Data to predict
              I need train a model which can predict for such input data
              input.txt
              3313321, 3259181, 2022230, 558310
7039277, 3454335, 2022230, 558310
5253403, 4207799, 2022231, 558310
4073770, 2445894, 2022230, 558310
6569923, 2445894, 2022230, 558310

              similar scheme.
              For example, in the above example for input.txt prediction would be quite if model say that sample #1 most similar for input scheme.
              Question
              Which algorithm from ML.NET should I use to solve my task? Of cause I do not expect complete solution, just put me right way.
              
I have a little bit sub-questions to clarify my problem:
              • How preparing dataset to train: by feature describe or matrix?
              • Before some classifier should I clustering data?
              Metadata
              Metadata
              Assignees
              No one assigned
                Labels
                No labels
                No labels
                Type
                No type
                Projects
                No projects
                  Milestone
                  No milestone
                  Relationships
                  None yet
                  Development
                  No branches or pull requests
                  Issue actions
                  
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

                  Skip to content
                  Predict similar scheme #680
                  Closed
                  Closed
                  Predict similar scheme#680
                  Description
                  @AdamShakhabov
                  AdamShakhabov
                  opened on Aug 15, 2018
                  Sorry for my English.
                  Dataset
                  There is dataset which contains files which describe scheme:
                  sample #1.txt
                  3103686, 2590304, 2022230, 838696
5530360, 1916721, 2022230, 430823
3103686, 3807071, 2022230, 430823
5705725, 4022485, 2022230, 975943
8043677, 3697167, 2022230, 430823
8043677, 2761756, 2022230, 430823

                  sample #2.txt
                  2994926, 3072910, 2022230, 1752477
7396944, 3072911, 2022230, 1752476
2994926, 1981531, 5573177, 558310

                  Each row is rectangle element (on scheme) feature vector (x, y, width, height).
                  Data to predict
                  I need train a model which can predict for such input data
                  input.txt
                  3313321, 3259181, 2022230, 558310
7039277, 3454335, 2022230, 558310
5253403, 4207799, 2022231, 558310
4073770, 2445894, 2022230, 558310
6569923, 2445894, 2022230, 558310

                  similar scheme.
                  For example, in the above example for input.txt prediction would be quite if model say that sample #1 most similar for input scheme.
                  Question
                  Which algorithm from ML.NET should I use to solve my task? Of cause I do not expect complete solution, just put me right way.
                  
I have a little bit sub-questions to clarify my problem:
                  • How preparing dataset to train: by feature describe or matrix?
                  • Before some classifier should I clustering data?
                  Metadata
                  Metadata
                  Assignees
                  No one assigned
                    Labels
                    No labels
                    No labels
                    Type
                    No type
                    Projects
                    No projects
                      Milestone
                      No milestone
                      Relationships
                      None yet
                      Development
                      No branches or pull requests
                      Issue actions
                      
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

                      Skip to content
                      Predict similar scheme #680
                      Closed
                      Closed
                      Predict similar scheme#680
                      Description
                      @AdamShakhabov
                      AdamShakhabov
                      opened on Aug 15, 2018
                      Sorry for my English.
                      Dataset
                      There is dataset which contains files which describe scheme:
                      sample #1.txt
                      3103686, 2590304, 2022230, 838696
5530360, 1916721, 2022230, 430823
3103686, 3807071, 2022230, 430823
5705725, 4022485, 2022230, 975943
8043677, 3697167, 2022230, 430823
8043677, 2761756, 2022230, 430823

                      sample #2.txt
                      2994926, 3072910, 2022230, 1752477
7396944, 3072911, 2022230, 1752476
2994926, 1981531, 5573177, 558310

                      Each row is rectangle element (on scheme) feature vector (x, y, width, height).
                      Data to predict
                      I need train a model which can predict for such input data
                      input.txt
                      3313321, 3259181, 2022230, 558310
7039277, 3454335, 2022230, 558310
5253403, 4207799, 2022231, 558310
4073770, 2445894, 2022230, 558310
6569923, 2445894, 2022230, 558310

                      similar scheme.
                      For example, in the above example for input.txt prediction would be quite if model say that sample #1 most similar for input scheme.
                      Question
                      Which algorithm from ML.NET should I use to solve my task? Of cause I do not expect complete solution, just put me right way.
                      
I have a little bit sub-questions to clarify my problem:
                      • How preparing dataset to train: by feature describe or matrix?
                      • Before some classifier should I clustering data?
                      Metadata
                      Metadata
                      Assignees
                      No one assigned
                        Labels
                        No labels
                        No labels
                        Type
                        No type
                        Projects
                        No projects
                          Milestone
                          No milestone
                          Relationships
                          None yet
                          Development
                          No branches or pull requests
                          Issue actions
                          
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

                          Skip to content
                          Predict similar scheme #680
                          Closed
                          Closed
                          Predict similar scheme#680
                          Description
                          @AdamShakhabov
                          AdamShakhabov
                          opened on Aug 15, 2018
                          Sorry for my English.
                          Dataset
                          There is dataset which contains files which describe scheme:
                          sample #1.txt
                          3103686, 2590304, 2022230, 838696
5530360, 1916721, 2022230, 430823
3103686, 3807071, 2022230, 430823
5705725, 4022485, 2022230, 975943
8043677, 3697167, 2022230, 430823
8043677, 2761756, 2022230, 430823

                          sample #2.txt
                          2994926, 3072910, 2022230, 1752477
7396944, 3072911, 2022230, 1752476
2994926, 1981531, 5573177, 558310

                          Each row is rectangle element (on scheme) feature vector (x, y, width, height).
                          Data to predict
                          I need train a model which can predict for such input data
                          input.txt
                          3313321, 3259181, 2022230, 558310
7039277, 3454335, 2022230, 558310
5253403, 4207799, 2022231, 558310
4073770, 2445894, 2022230, 558310
6569923, 2445894, 2022230, 558310

                          similar scheme.
                          For example, in the above example for input.txt prediction would be quite if model say that sample #1 most similar for input scheme.
                          Question
                          Which algorithm from ML.NET should I use to solve my task? Of cause I do not expect complete solution, just put me right way.
                          
I have a little bit sub-questions to clarify my problem:
                          • How preparing dataset to train: by feature describe or matrix?
                          • Before some classifier should I clustering data?
                          Metadata
                          Metadata
                          Assignees
                          No one assigned
                            Labels
                            No labels
                            No labels
                            Type
                            No type
                            Projects
                            No projects
                              Milestone
                              No milestone
                              Relationships
                              None yet
                              Development
                              No branches or pull requests
                              Issue actions
                              
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

                              Skip to content
                              Predict similar scheme #680
                              Closed
                              Closed
                              Predict similar scheme#680
                              Description
                              @AdamShakhabov
                              AdamShakhabov
                              opened on Aug 15, 2018
                              Sorry for my English.
                              Dataset
                              There is dataset which contains files which describe scheme:
                              sample #1.txt
                              3103686, 2590304, 2022230, 838696
5530360, 1916721, 2022230, 430823
3103686, 3807071, 2022230, 430823
5705725, 4022485, 2022230, 975943
8043677, 3697167, 2022230, 430823
8043677, 2761756, 2022230, 430823

                              sample #2.txt
                              2994926, 3072910, 2022230, 1752477
7396944, 3072911, 2022230, 1752476
2994926, 1981531, 5573177, 558310

                              Each row is rectangle element (on scheme) feature vector (x, y, width, height).
                              Data to predict
                              I need train a model which can predict for such input data
                              input.txt
                              3313321, 3259181, 2022230, 558310
7039277, 3454335, 2022230, 558310
5253403, 4207799, 2022231, 558310
4073770, 2445894, 2022230, 558310
6569923, 2445894, 2022230, 558310

                              similar scheme.
                              For example, in the above example for input.txt prediction would be quite if model say that sample #1 most similar for input scheme.
                              Question
                              Which algorithm from ML.NET should I use to solve my task? Of cause I do not expect complete solution, just put me right way.
                              
I have a little bit sub-questions to clarify my problem:
                              • How preparing dataset to train: by feature describe or matrix?
                              • Before some classifier should I clustering data?
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