From ffabf62b4dd1760fdcf77296fcedb689ffbd6479 Mon Sep 17 00:00:00 2001
From: archmishka <kopevmihail7@gmail.com>
Date: Fri, 12 Jun 2026 20:02:33 +0300
Subject: [PATCH 1/4] Add algorithms

---
 python/algorithms/bfs_classic.py          |    20 +
 python/algorithms/bfs_result.txt          | 14824 +++++++++++++++++++
 python/algorithms/bfs_spla.py             |    77 +
 python/algorithms/bfs_spla_result.txt     | 14824 +++++++++++++++++++
 python/algorithms/cohen_result.txt        |     2 +
 python/algorithms/fs_183_1/fs_183_1.rb    |   427 +
 python/algorithms/fs_183_1/fs_183_1.txt   |    15 +
 python/algorithms/graph.txt               |     3 +
 python/algorithms/graph_classic.py        |   115 +
 python/algorithms/graph_spla.py           |   164 +
 python/algorithms/main_classic.py         |    77 +
 python/algorithms/main_spla.py            |    79 +
 python/algorithms/matrix.txt              |     3 +
 python/algorithms/pr_classic.py           |    26 +
 python/algorithms/pr_result.txt           | 14826 +++++++++++++++++++
 python/algorithms/pr_spla.py              |    22 +
 python/algorithms/result.txt              | 14827 ++++++++++++++++++++
 python/algorithms/result_classic.txt      |     4 +
 python/algorithms/result_sssp_classic.txt |     4 +
 python/algorithms/sssp_classic.py         |    14 +
 python/algorithms/sssp_spla.py            |    30 +
 python/algorithms/tc_classic.py           |    11 +
 python/algorithms/tc_spla.py              |    31 +
 python/algorithms/test_compare.py         |   203 +
 24 files changed, 60628 insertions(+)
 create mode 100644 python/algorithms/bfs_classic.py
 create mode 100644 python/algorithms/bfs_result.txt
 create mode 100644 python/algorithms/bfs_spla.py
 create mode 100644 python/algorithms/bfs_spla_result.txt
 create mode 100644 python/algorithms/cohen_result.txt
 create mode 100644 python/algorithms/fs_183_1/fs_183_1.rb
 create mode 100644 python/algorithms/fs_183_1/fs_183_1.txt
 create mode 100644 python/algorithms/graph.txt
 create mode 100644 python/algorithms/graph_classic.py
 create mode 100644 python/algorithms/graph_spla.py
 create mode 100644 python/algorithms/main_classic.py
 create mode 100644 python/algorithms/main_spla.py
 create mode 100644 python/algorithms/matrix.txt
 create mode 100644 python/algorithms/pr_classic.py
 create mode 100644 python/algorithms/pr_result.txt
 create mode 100644 python/algorithms/pr_spla.py
 create mode 100644 python/algorithms/result.txt
 create mode 100644 python/algorithms/result_classic.txt
 create mode 100644 python/algorithms/result_sssp_classic.txt
 create mode 100644 python/algorithms/sssp_classic.py
 create mode 100644 python/algorithms/sssp_spla.py
 create mode 100644 python/algorithms/tc_classic.py
 create mode 100644 python/algorithms/tc_spla.py
 create mode 100644 python/algorithms/test_compare.py

diff --git a/python/algorithms/bfs_classic.py b/python/algorithms/bfs_classic.py
new file mode 100644
index 000000000..582c5a51b
--- /dev/null
+++ b/python/algorithms/bfs_classic.py
@@ -0,0 +1,20 @@
+from collections import deque
+
+INF = int(1e9)
+
+def bfs(start, graph, n):
+    visited = [0] * n
+    dist = [INF] * n
+    q = deque()
+    visited[start] = 1
+    dist[start] = 0
+    q.append(start)
+    
+    while q:
+        u = q.popleft()
+        for v in graph[u]:
+            if not visited[v]:
+                visited[v] = 1
+                dist[v] = dist[u] + 1
+                q.append(v)
+    return dist
\ No newline at end of file
diff --git a/python/algorithms/bfs_result.txt b/python/algorithms/bfs_result.txt
new file mode 100644
index 000000000..aba2ac14a
--- /dev/null
+++ b/python/algorithms/bfs_result.txt
@@ -0,0 +1,14824 @@
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diff --git a/python/algorithms/bfs_spla.py b/python/algorithms/bfs_spla.py
new file mode 100644
index 000000000..de3ead706
--- /dev/null
+++ b/python/algorithms/bfs_spla.py
@@ -0,0 +1,77 @@
+import sys
+import argparse
+from pathlib import Path
+from pyspla import *
+
+def bfs(s: int, A: Matrix):
+    v = Vector(A.n_rows, INT)
+    front = Vector.from_lists([s], [1], A.n_rows, INT)
+    front_size = 1
+    depth = Scalar(INT, 0)
+    count = 0
+
+    while front_size > 0:
+        depth += 1
+        count += front_size
+        v.assign(front, depth, op_assign=INT.SECOND, op_select=INT.NQZERO)
+        front = front.vxm(v, A, op_mult=INT.LAND, op_add=INT.LOR, op_select=INT.EQZERO)
+        front_size = front.reduce(op_reduce=INT.PLUS).get()
+
+    return v, count, depth.get()
+
+def read_vectors(filename):
+    with open(filename) as f:
+        I = list(map(int, f.readline().replace(',', ' ').split()))
+        J = list(map(int, f.readline().replace(',', ' ').split()))
+        V = list(map(int, f.readline().replace(',', ' ').split()))
+    rows = int(max(I) + 1)
+    cols = int(max(J) + 1)
+    return Matrix.from_lists(I, J, V, shape=(rows, cols), dtype=INT)
+
+def read_mtx(filename):
+    I, J, V = [], [], []
+    n_vertices = 0
+    with open(filename, 'r') as f:
+        for line in f:
+            if line.startswith('%'):
+                continue
+            parts = line.split()
+            if n_vertices == 0:
+                n_vertices = int(parts[0])
+                continue
+            i = int(parts[0]) - 1
+            j = int(parts[1]) - 1
+            I.append(i); J.append(j); V.append(1)
+            if i != j:
+                I.append(j); J.append(i); V.append(1)
+    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=INT)
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-m", "--matrix", type=Path)
+    parser.add_argument("-v", "--vectors", type=Path)
+    parser.add_argument("-s", "--start", type=int, default=0)
+    parser.add_argument("-o", "--output", type=Path, default="bfs_spla_result.txt")
+    args = parser.parse_args()
+
+    if args.matrix and args.matrix.exists():
+        A = read_mtx(str(args.matrix))
+    elif args.vectors and args.vectors.exists():
+        A = read_vectors(str(args.vectors))
+    else:
+        sys.exit(1)
+
+    v, count, depth = bfs(args.start, A)
+
+    with open(args.output, 'w') as f_out:
+        f_out.write(f"Reached vertices: {count}\n")
+        f_out.write(f"Max depth: {depth - 1}\n")
+        indices, values = v.to_lists()
+        for i, dist in zip(indices, values):
+            f_out.write(f"{i} {dist - 1}\n")
+
+    print(f"Result written to {args.output}")
+
+if __name__ == "__main__":
+    import os
+    main()
\ No newline at end of file
diff --git a/python/algorithms/bfs_spla_result.txt b/python/algorithms/bfs_spla_result.txt
new file mode 100644
index 000000000..aba2ac14a
--- /dev/null
+++ b/python/algorithms/bfs_spla_result.txt
@@ -0,0 +1,14824 @@
+Reached vertices: 14822
+Max depth: 46
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diff --git a/python/algorithms/cohen_result.txt b/python/algorithms/cohen_result.txt
new file mode 100644
index 000000000..81a98f9a1
--- /dev/null
+++ b/python/algorithms/cohen_result.txt
@@ -0,0 +1,2 @@
+Matrix: Pres_Poisson.mtx
+Triangles: 3229525
diff --git a/python/algorithms/fs_183_1/fs_183_1.rb b/python/algorithms/fs_183_1/fs_183_1.rb
new file mode 100644
index 000000000..0210c33de
--- /dev/null
+++ b/python/algorithms/fs_183_1/fs_183_1.rb
@@ -0,0 +1,427 @@
+HB/fs_183_1; 1983; A. Curtis; ed: I. Duff et al.                       |141     
+           423            12            54           357
+rua                      183           183          1069             0
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+  207  210  213  216  224  232  239  248  255  264  270  275  280  287  292  296
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diff --git a/python/algorithms/fs_183_1/fs_183_1.txt b/python/algorithms/fs_183_1/fs_183_1.txt
new file mode 100644
index 000000000..e55f31d71
--- /dev/null
+++ b/python/algorithms/fs_183_1/fs_183_1.txt
@@ -0,0 +1,15 @@
+%-------------------------------------------------------------------------------
+% UF Sparse Matrix Collection, Tim Davis
+% http://www.cise.ufl.edu/research/sparse/matrices/HB/fs_183_1
+% name: HB/fs_183_1
+% [UNSYMMETRIC FACSIMILE CONVERGENCE MATRIX]
+% id: 141
+% date: 1983
+% author: A. Curtis
+% ed: I. Duff, R. Grimes, J. Lewis
+% fields: title A Zeros name id kind notes date author ed
+% kind: 2D/3D problem sequence
+%-------------------------------------------------------------------------------
+% notes:
+% next: HB/fs_183_3 first: HB/fs_183_1
+%-------------------------------------------------------------------------------
diff --git a/python/algorithms/graph.txt b/python/algorithms/graph.txt
new file mode 100644
index 000000000..bceed69ff
--- /dev/null
+++ b/python/algorithms/graph.txt
@@ -0,0 +1,3 @@
+0 0 1 1 2 2 2 3
+1 2 0 2 0 1 3 2
+1 1 1 1 1 1 1 1
diff --git a/python/algorithms/graph_classic.py b/python/algorithms/graph_classic.py
new file mode 100644
index 000000000..ce66053de
--- /dev/null
+++ b/python/algorithms/graph_classic.py
@@ -0,0 +1,115 @@
+from collections import defaultdict
+
+def read_mtx_unweighted(filename):
+    n = 0
+    graph = defaultdict(list)
+    with open(filename, 'r') as f:
+        for line in f:
+            if line.startswith('%'):
+                continue
+            parts = line.split()
+            if n == 0:
+                n = int(parts[0])
+                continue
+            i = int(parts[0]) - 1
+            j = int(parts[1]) - 1
+            graph[i].append(j)
+            if i != j:
+                graph[j].append(i)
+    return graph, n
+
+def read_vectors_unweighted(filename):
+    with open(filename, 'r') as f:
+        I = list(map(int, f.readline().replace(',', ' ').split()))
+        J = list(map(int, f.readline().replace(',', ' ').split()))
+        
+    n = max(max(I), max(J)) + 1 if I else 0
+    graph = defaultdict(list)
+    
+    for k in range(len(I)):
+        i = I[k]
+        j = J[k]
+        graph[i].append(j)
+        if i != j:
+            graph[j].append(i)
+            
+    return graph, n
+
+def read_mtx_weighted(filename):
+    n = 0
+    graph = defaultdict(list)
+    with open(filename, 'r') as f:
+        for line in f:
+            if line.startswith('%'):
+                continue
+            parts = line.split()
+            if n == 0:
+                n = int(parts[0])
+                continue
+            i = int(parts[0]) - 1
+            j = int(parts[1]) - 1
+            v = float(parts[2]) if len(parts) > 2 else 1.0
+            graph[i].append((j, v))
+            if i != j:
+                graph[j].append((i, v))
+    return graph, n
+
+def read_vectors_weighted(filename):
+    with open(filename, 'r') as f:
+        I = list(map(int, f.readline().replace(',', ' ').split()))
+        J = list(map(int, f.readline().replace(',', ' ').split()))
+        V = list(map(float, f.readline().replace(',', ' ').split()))
+        
+    n = max(max(I), max(J)) + 1 if I else 0
+    graph = defaultdict(list)
+    
+    for k in range(len(I)):
+        i = I[k]
+        j = J[k]
+        v = V[k]
+        graph[i].append((j, v))
+        if i != j:
+            graph[j].append((i, v))
+            
+    return graph, n
+
+def read_mtx_pr_classic(filename):
+    n = 0
+    adj_in = defaultdict(list)
+    with open(filename, 'r') as f:
+        for line in f:
+            if line.startswith('%'):
+                continue
+            parts = line.split()
+            if n == 0:
+                n = int(parts[0])
+                out_degree = [0] * n
+                continue
+            i = int(parts[0]) - 1
+            j = int(parts[1]) - 1
+            adj_in[j].append(i)
+            out_degree[i] += 1
+            if i != j:
+                adj_in[i].append(j)
+                out_degree[j] += 1
+    return adj_in, out_degree, n
+
+def read_vectors_pr_classic(filename):
+    with open(filename, 'r') as f:
+        I = list(map(int, f.readline().replace(',', ' ').split()))
+        J = list(map(int, f.readline().replace(',', ' ').split()))
+        
+    n = max(max(I), max(J)) + 1 if I else 0
+    adj_in = defaultdict(list)
+    out_degree = [0] * n
+    
+    for k in range(len(I)):
+        i = I[k]
+        j = J[k]
+        adj_in[j].append(i)
+        out_degree[i] += 1
+        if i != j:
+            adj_in[i].append(j)
+            out_degree[j] += 1
+            
+    return adj_in, out_degree, n
\ No newline at end of file
diff --git a/python/algorithms/graph_spla.py b/python/algorithms/graph_spla.py
new file mode 100644
index 000000000..5f9f19a1b
--- /dev/null
+++ b/python/algorithms/graph_spla.py
@@ -0,0 +1,164 @@
+from pyspla import *
+
+def read_mtx_int(filename):
+    I, J, V = [], [], []
+    n_vertices = 0
+    
+    with open(filename, 'r') as f:
+        for line in f:
+            if line.startswith('%'):
+                continue
+            parts = line.split()
+            if n_vertices == 0:
+                n_vertices = int(parts[0])
+                continue
+                
+            i = int(parts[0]) - 1
+            j = int(parts[1]) - 1
+            
+            I.append(i)
+            J.append(j)
+            V.append(1)
+            
+            if i != j:
+                I.append(j)
+                J.append(i)
+                V.append(1)
+                
+    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=INT)
+
+def read_vectors_int(filename):
+    with open(filename, 'r') as f:
+        raw_I = list(map(int, f.readline().replace(',', ' ').split()))
+        raw_J = list(map(int, f.readline().replace(',', ' ').split()))
+        
+    n_vertices = max(max(raw_I), max(raw_J)) + 1
+    I, J, V = [], [], []
+    
+    for k in range(len(raw_I)):
+        i = raw_I[k]
+        j = raw_J[k]
+        
+        I.append(i)
+        J.append(j)
+        V.append(1)
+        
+        if i != j:
+            I.append(j)
+            J.append(i)
+            V.append(1)
+            
+    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=INT)
+
+def read_mtx_float(filename):
+    I, J, V = [], [], []
+    n_vertices = 0
+    
+    with open(filename, 'r') as f:
+        for line in f:
+            if line.startswith('%'):
+                continue
+            parts = line.split()
+            if n_vertices == 0:
+                n_vertices = int(parts[0])
+                continue
+                
+            i = int(parts[0]) - 1
+            j = int(parts[1]) - 1
+            weight = float(parts[2]) if len(parts) > 2 else 1.0
+            
+            I.append(i)
+            J.append(j)
+            V.append(weight)
+            
+            if i != j:
+                I.append(j)
+                J.append(i)
+                V.append(weight)
+                
+    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=FLOAT)
+
+def read_vectors_float(filename):
+    with open(filename, 'r') as f:
+        raw_I = list(map(int, f.readline().replace(',', ' ').split()))
+        raw_J = list(map(int, f.readline().replace(',', ' ').split()))
+        raw_V = list(map(float, f.readline().replace(',', ' ').split()))
+        
+    n_vertices = max(max(raw_I), max(raw_J)) + 1
+    I, J, V = [], [], []
+    
+    for k in range(len(raw_I)):
+        i = raw_I[k]
+        j = raw_J[k]
+        weight = raw_V[k]
+        
+        I.append(i)
+        J.append(j)
+        V.append(weight)
+        
+        if i != j:
+            I.append(j)
+            J.append(i)
+            V.append(weight)
+            
+    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=FLOAT)
+
+def read_mtx_pr(filename, alpha):
+    I, J, V = [], [], []
+    n_vertices = 0
+    
+    with open(filename, 'r') as f:
+        for line in f:
+            if line.startswith('%'):
+                continue
+            parts = line.split()
+            if n_vertices == 0:
+                n_vertices = int(parts[0])
+                out_degree = [0] * n_vertices
+                continue
+                
+            i = int(parts[0]) - 1
+            j = int(parts[1]) - 1
+            
+            I.append(i)
+            J.append(j)
+            out_degree[i] += 1
+            
+            if i != j:
+                I.append(j)
+                J.append(i)
+                out_degree[j] += 1
+                
+    for k in range(len(I)):
+        u = I[k]
+        V.append(alpha / out_degree[u])
+        
+    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=FLOAT)
+
+def read_vectors_pr(filename, alpha):
+    with open(filename, 'r') as f:
+        raw_I = list(map(int, f.readline().replace(',', ' ').split()))
+        raw_J = list(map(int, f.readline().replace(',', ' ').split()))
+        
+    n_vertices = max(max(raw_I), max(raw_J)) + 1
+    out_degree = [0] * n_vertices
+    I, J, V = [], [], []
+    
+    for k in range(len(raw_I)):
+        i = raw_I[k]
+        j = raw_J[k]
+        
+        I.append(i)
+        J.append(j)
+        out_degree[i] += 1
+        
+        if i != j:
+            I.append(j)
+            J.append(i)
+            out_degree[j] += 1
+            
+    for k in range(len(I)):
+        u = I[k]
+        V.append(alpha / out_degree[u])
+            
+    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=FLOAT)
\ No newline at end of file
diff --git a/python/algorithms/main_classic.py b/python/algorithms/main_classic.py
new file mode 100644
index 000000000..1d7c778de
--- /dev/null
+++ b/python/algorithms/main_classic.py
@@ -0,0 +1,77 @@
+import sys
+import argparse
+from pathlib import Path
+
+from bfs_classic import bfs, INF as BFS_INF
+from sssp_classic import sssp, INF as SSSP_INF
+from pr_classic import pagerank_classic
+from tc_classic import tc_simple
+
+from graph_classic import read_mtx_unweighted, read_vectors_unweighted
+from graph_classic import read_mtx_weighted, read_vectors_weighted
+from graph_classic import read_mtx_pr_classic, read_vectors_pr_classic
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--algo", choices=["bfs", "sssp", "pr", "tc"], required=True)
+    parser.add_argument("-m", "--matrix", type=Path)
+    parser.add_argument("-v", "--vectors", type=Path)
+    parser.add_argument("-o", "--output", type=Path, default="result_classic.txt")
+    parser.add_argument("-s", "--start", type=int, default=0)
+    parser.add_argument("-a", "--alpha", type=float, default=0.85)
+    parser.add_argument("-e", "--eps", type=float, default=1e-4)
+    args = parser.parse_args()
+
+    if not args.matrix and not args.vectors:
+        sys.exit("Error: no graph file provided")
+
+    if args.algo == "bfs":
+        if args.matrix:
+            graph, n = read_mtx_unweighted(str(args.matrix))
+        else:
+            graph, n = read_vectors_unweighted(str(args.vectors))
+        distances = bfs(args.start, graph, n)
+        with open(args.output, 'w') as f_out:
+            for i in range(len(distances)):
+                if distances[i] != BFS_INF:
+                    f_out.write(f"{i} {distances[i]}\n")
+        print(f"Result saved to {args.output}")
+
+    elif args.algo == "sssp":
+        if args.matrix:
+            graph, n = read_mtx_weighted(str(args.matrix))
+        else:
+            graph, n = read_vectors_weighted(str(args.vectors))
+        distances = sssp(args.start, graph, n)
+        with open(args.output, 'w') as f_out:
+            for i in range(len(distances)):
+                if distances[i] != SSSP_INF:
+                    f_out.write(f"{i} {distances[i]}\n")
+        print(f"Result saved to {args.output}")
+
+    elif args.algo == "pr":
+        if args.matrix:
+            adj_in, out_degree, n = read_mtx_pr_classic(str(args.matrix))
+        else:
+            adj_in, out_degree, n = read_vectors_pr_classic(str(args.vectors))
+        p, iters = pagerank_classic(adj_in, out_degree, n, args.alpha, args.eps)
+        with open(args.output, 'w') as f_out:
+            f_out.write(f"Iterations: {iters}\n")
+            for i in range(len(p)):
+                f_out.write(f"{i} {p[i]:.6f}\n")
+        print(f"Result saved to {args.output}")
+
+    elif args.algo == "tc":
+        if args.matrix:
+            graph, n = read_mtx_unweighted(str(args.matrix))
+        else:
+            graph, n = read_vectors_unweighted(str(args.vectors))
+        for i in range(n):
+            graph[i].sort()
+        triangles = tc_simple(graph, n)
+        with open(args.output, 'w') as f_out:
+            f_out.write(f"Triangles: {triangles}\n")
+        print(f"Result saved to {args.output}")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/python/algorithms/main_spla.py b/python/algorithms/main_spla.py
new file mode 100644
index 000000000..8c1d38dfc
--- /dev/null
+++ b/python/algorithms/main_spla.py
@@ -0,0 +1,79 @@
+import sys
+import argparse
+from pathlib import Path
+
+from bfs_spla import bfs
+from sssp_spla import sssp, INF
+from pr_spla import pagerank
+from tc_spla import cohen
+
+from graph_spla import read_mtx_int, read_vectors_int
+from graph_spla import read_mtx_float, read_vectors_float
+from graph_spla import read_mtx_pr, read_vectors_pr
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--algo", choices=["bfs", "sssp", "pr", "tc"], required=True)
+    parser.add_argument("-m", "--matrix", type=Path)
+    parser.add_argument("-v", "--vectors", type=Path)
+    parser.add_argument("-o", "--output", type=Path, default="result.txt")
+    parser.add_argument("-s", "--start", type=int, default=0)
+    parser.add_argument("-a", "--alpha", type=float, default=0.85)
+    parser.add_argument("-e", "--eps", type=float, default=1e-4)
+    args = parser.parse_args()
+
+    if not args.matrix and not args.vectors:
+        sys.exit("Error: no graph file provided")
+
+    if args.algo == "bfs":
+        if args.matrix:
+            A = read_mtx_int(str(args.matrix))
+        else:
+            A = read_vectors_int(str(args.vectors))
+        v, count, depth = bfs(args.start, A)
+        with open(args.output, 'w') as f_out:
+            f_out.write(f"Reached vertices: {count}\n")
+            f_out.write(f"Max depth: {depth - 1}\n")
+            idx, vals = v.to_lists()
+            for k in range(len(idx)):
+                f_out.write(f"{idx[k]} {vals[k] - 1}\n")
+        print(f"Result saved to {args.output}")
+
+    elif args.algo == "sssp":
+        if args.matrix:
+            A = read_mtx_float(str(args.matrix))
+        else:
+            A = read_vectors_float(str(args.vectors))
+        v = sssp(args.start, A)
+        with open(args.output, 'w') as f_out:
+            idx, vals = v.to_lists()
+            for k in range(len(idx)):
+                if vals[k] < INF:
+                    f_out.write(f"{idx[k]} {vals[k]}\n")
+        print(f"Result saved to {args.output}")
+
+    elif args.algo == "pr":
+        if args.matrix:
+            A = read_mtx_pr(str(args.matrix), args.alpha)
+        else:
+            A = read_vectors_pr(str(args.vectors), args.alpha)
+        p, iters = pagerank(A, args.alpha, args.eps)
+        with open(args.output, 'w') as f_out:
+            f_out.write(f"Iterations: {iters}\n")
+            idx, vals = p.to_lists()
+            for k in range(len(idx)):
+                f_out.write(f"{idx[k]} {vals[k]:.6f}\n")
+        print(f"Result saved to {args.output}")
+
+    elif args.algo == "tc":
+        if args.matrix:
+            A = read_mtx_int(str(args.matrix))
+        else:
+            A = read_vectors_int(str(args.vectors))
+        triangles = cohen(A)
+        with open(args.output, 'w') as f_out:
+            f_out.write(f"Triangles: {triangles}\n")
+        print(f"Result saved to {args.output}")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/python/algorithms/matrix.txt b/python/algorithms/matrix.txt
new file mode 100644
index 000000000..5b2597053
--- /dev/null
+++ b/python/algorithms/matrix.txt
@@ -0,0 +1,3 @@
+0 1 2 2 3
+1 2 0 3 2
+1 1 1 1 1
diff --git a/python/algorithms/pr_classic.py b/python/algorithms/pr_classic.py
new file mode 100644
index 000000000..9c7262f18
--- /dev/null
+++ b/python/algorithms/pr_classic.py
@@ -0,0 +1,26 @@
+import math
+
+def pagerank_classic(adj_in, out_degree, n, alpha, eps):
+    p = [1.0 / n] * n
+    addition = (1.0 - alpha) / n
+    error = eps + 1.0
+    iterations = 0
+
+    while error > eps:
+        p_next = [0.0] * n
+        for i in range(n):
+            sum_pr = 0.0
+            for j in adj_in[i]:
+                sum_pr += p[j] / out_degree[j]
+            p_next[i] = alpha * sum_pr + addition
+
+        error2 = 0.0
+        for i in range(n):
+            diff = p_next[i] - p[i]
+            error2 += diff * diff
+
+        error = math.sqrt(error2)
+        p = p_next
+        iterations += 1
+
+    return p, iterations
\ No newline at end of file
diff --git a/python/algorithms/pr_result.txt b/python/algorithms/pr_result.txt
new file mode 100644
index 000000000..ce8be4b1f
--- /dev/null
+++ b/python/algorithms/pr_result.txt
@@ -0,0 +1,14826 @@
+--- PageRank (Pres_Poisson.mtx) ---
+alpha=0.85, eps=0.0001
+Iterations: 2
+Vertex | Rank
+0 0.000010
+1 0.000010
+2 0.000010
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diff --git a/python/algorithms/pr_spla.py b/python/algorithms/pr_spla.py
new file mode 100644
index 000000000..c7228bda6
--- /dev/null
+++ b/python/algorithms/pr_spla.py
@@ -0,0 +1,22 @@
+import math
+from pyspla import *
+
+def pagerank(A: Matrix, alpha: float, eps: float):
+    N = A.n_rows
+    p = Vector.dense(N, FLOAT, 1.0 / N)
+    addition = Vector.dense(N, FLOAT, (1.0 - alpha) / N)
+    dummy_mask = Vector.dense(N, FLOAT, 1.0)
+    zero = Scalar(FLOAT, 0.0)
+    error = eps + 1.0
+    iterations = 0
+    
+    while error > eps:
+        p_prev = p
+        p_tmp = p_prev.vxm(dummy_mask, A, op_mult=FLOAT.MULT, op_add=FLOAT.PLUS, op_select=FLOAT.ALWAYS, init=zero)
+        p = p_tmp.eadd(FLOAT.PLUS, addition)
+        diff = p.eadd(FLOAT.MINUS_POW2, p_prev)
+        error2 = diff.reduce(FLOAT.PLUS, init=zero)
+        error = math.sqrt(error2.get())
+        iterations += 1
+        
+    return p, iterations
\ No newline at end of file
diff --git a/python/algorithms/result.txt b/python/algorithms/result.txt
new file mode 100644
index 000000000..e11bebdec
--- /dev/null
+++ b/python/algorithms/result.txt
@@ -0,0 +1,14827 @@
+--- BFS results for matrix Pres_Poisson.mtx ---
+Reached vertices: 14822
+Max depth: 46
+----------------------------------------
+Vertex | Distance
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+1 2
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diff --git a/python/algorithms/result_classic.txt b/python/algorithms/result_classic.txt
new file mode 100644
index 000000000..d7779cac3
--- /dev/null
+++ b/python/algorithms/result_classic.txt
@@ -0,0 +1,4 @@
+0 0.0
+1 1.0
+2 1.0
+3 2.0
diff --git a/python/algorithms/result_sssp_classic.txt b/python/algorithms/result_sssp_classic.txt
new file mode 100644
index 000000000..f51c640f8
--- /dev/null
+++ b/python/algorithms/result_sssp_classic.txt
@@ -0,0 +1,4 @@
+0 0
+1 1.0
+2 1.0
+3 2.0
diff --git a/python/algorithms/sssp_classic.py b/python/algorithms/sssp_classic.py
new file mode 100644
index 000000000..eb8766915
--- /dev/null
+++ b/python/algorithms/sssp_classic.py
@@ -0,0 +1,14 @@
+INF = 1e9
+
+def sssp(start, graph, n):
+    dist = [INF] * n
+    dist[start] = 0
+
+    for i in range(n - 1):
+        for u in range(n):
+            if dist[u] != INF:
+                for v, w in graph[u]:
+                    dist[v] = min(dist[v], dist[u] + w)
+                        
+    return dist
+
diff --git a/python/algorithms/sssp_spla.py b/python/algorithms/sssp_spla.py
new file mode 100644
index 000000000..af1a54211
--- /dev/null
+++ b/python/algorithms/sssp_spla.py
@@ -0,0 +1,30 @@
+from pyspla import Matrix, Vector, FLOAT, Scalar
+
+INF = 1e9
+
+def sssp_broken(start: int, A: Matrix):
+    N = A.n_rows
+    v = Vector(N, FLOAT)
+    v.set(start, 0.0)
+    
+    frontier = Vector(N, FLOAT)
+    frontier.set(start, 0.0)
+    
+    dummy_mask = Vector.dense(N, FLOAT, 1.0)
+    inf_scalar = Scalar(FLOAT, INF)
+    
+    step = 0
+    front_size = 1 
+    while front_size > 0 and step < N:
+        frontier = frontier.vxm(dummy_mask, A, op_mult=FLOAT.PLUS, op_add=FLOAT.MIN, op_select=FLOAT.ALWAYS, init=inf_scalar)
+        v = v.eadd(FLOAT.MIN, frontier)
+        idx, vals = v.to_lists()
+        print(f"Iteration {step} V: {sorted(list(zip(idx, vals)))}")
+        idx2, vals2 = frontier.to_lists()
+        print(f"Iteration {step} Frontier: {sorted(list(zip(idx2, vals2)))}")
+        front_size = frontier.reduce(FLOAT.PLUS).get()
+        print(f"Iteration {step} Frontier sum: {front_size}")
+        print('-' * 24)
+        step += 1
+    
+    return v
\ No newline at end of file
diff --git a/python/algorithms/tc_classic.py b/python/algorithms/tc_classic.py
new file mode 100644
index 000000000..f60ed7b72
--- /dev/null
+++ b/python/algorithms/tc_classic.py
@@ -0,0 +1,11 @@
+def tc_simple(graph, n):
+    triangles = 0
+
+    for i in range(n):
+        for j in graph[i]:
+            for k in graph[j]:
+                if k in graph[i]:
+                    triangles += 1
+
+    return triangles // 6
+
diff --git a/python/algorithms/tc_spla.py b/python/algorithms/tc_spla.py
new file mode 100644
index 000000000..02fafa4fa
--- /dev/null
+++ b/python/algorithms/tc_spla.py
@@ -0,0 +1,31 @@
+from pyspla import *
+
+def split_into_lower_upper(A: Matrix):
+    I, J, V = A.to_lists()
+    L_I, L_J, L_V = [], [], []
+    U_I, U_J, U_V = [], [], []
+
+    for k in range(len(I)):
+        i = I[k]
+        j = J[k]
+        val = V[k]
+        if i < j:
+            U_I.append(i)
+            U_J.append(j)
+            U_V.append(val)
+        elif i > j:
+            L_I.append(i)
+            L_J.append(j)
+            L_V.append(val)
+
+    rows, cols = A.shape
+    lower = Matrix.from_lists(L_I, L_J, L_V, shape=(rows, cols), dtype=INT)
+    upper = Matrix.from_lists(U_I, U_J, U_V, shape=(rows, cols), dtype=INT)
+    return lower, upper
+
+def cohen(A: Matrix):
+    L, U = split_into_lower_upper(A)
+    B = L.mxm(M=U, op_mult=INT.MULT, op_add=INT.PLUS)
+    C = A.emult(op_mult=INT.MULT, M=B)
+    total_sum = C.reduce(op_reduce=INT.PLUS).get()
+    return total_sum // 2
\ No newline at end of file
diff --git a/python/algorithms/test_compare.py b/python/algorithms/test_compare.py
new file mode 100644
index 000000000..2cd814966
--- /dev/null
+++ b/python/algorithms/test_compare.py
@@ -0,0 +1,203 @@
+import unittest
+import math
+from collections import defaultdict
+from pyspla import Matrix, INT, FLOAT
+
+from bfs_classic import bfs as bfs_c, INF as BFS_INF
+from sssp_classic import sssp as sssp_c, INF as SSSP_INF
+from pr_classic import pagerank_classic as pr_c
+from tc_classic import tc_simple as tc_c
+
+from bfs_spla import bfs as bfs_s
+from sssp_spla import sssp as sssp_s
+from pr_spla import pagerank as pr_s
+from tc_spla import cohen as tc_s
+
+class TestCompareAlgorithms(unittest.TestCase):
+
+    # ==========================================
+    # 1. ПРОСТЫЕ ФУНКЦИИ СОЗДАНИЯ ГРАФОВ
+    # ==========================================
+
+    def build_bfs_graph(self, edges, n):
+        graph_c = defaultdict(list)
+        I, J, V = [], [], []
+        for u, v in edges:
+            graph_c[u].append(v)
+            graph_c[v].append(u)
+            I.extend([u, v])
+            J.extend([v, u])
+            V.extend([1, 1])
+        return graph_c, Matrix.from_lists(I, J, V, shape=(n, n), dtype=INT)
+
+    def build_sssp_graph(self, edges, n):
+        graph_c = defaultdict(list)
+        I, J, V = [], [], []
+        for u, v, w in edges:
+            graph_c[u].append((v, w))
+            graph_c[v].append((u, w))
+            I.extend([u, v])
+            J.extend([v, u])
+            V.extend([w, w])
+        return graph_c, Matrix.from_lists(I, J, V, shape=(n, n), dtype=INT)
+
+    def build_tc_graph(self, edges, n):
+        graph_c = defaultdict(list)
+        I, J, V = [], [], []
+        for u, v in edges:
+            graph_c[u].append(v)
+            graph_c[v].append(u)
+            I.extend([u, v])
+            J.extend([v, u])
+            V.extend([1, 1])
+        return graph_c, Matrix.from_lists(I, J, V, shape=(n, n), dtype=INT)
+
+    def build_pr_graph(self, edges, n, alpha=0.85):
+        adj_in = defaultdict(list)
+        out_degree = [0] * n
+        I, J, V = [], [], []
+        
+        # Сначала считаем исходящие ребра
+        for u, v in edges:
+            out_degree[u] += 1
+            if u != v:
+                out_degree[v] += 1
+                
+        # Затем строим графы
+        for u, v in edges:
+            adj_in[v].append(u)
+            I.append(u); J.append(v); V.append(alpha / out_degree[u])
+            if u != v:
+                adj_in[u].append(v)
+                I.append(v); J.append(u); V.append(alpha / out_degree[v])
+                
+        return adj_in, out_degree, Matrix.from_lists(I, J, V, shape=(n, n), dtype=FLOAT)
+
+    # ==========================================
+    # 2. ФУНКЦИИ СРАВНЕНИЯ РЕЗУЛЬТАТОВ
+    # ==========================================
+
+    def check_lists_equal(self, list1, list2):
+        self.assertEqual(len(list1), len(list2))
+        for k in range(len(list1)):
+            self.assertEqual(list1[k], list2[k], f"Mismatch at index {k}: {list1[k]} != {list2[k]}")
+
+    def check_lists_close(self, list1, list2, tol=1e-3):
+        self.assertEqual(len(list1), len(list2))
+        for k in range(len(list1)):
+            self.assertTrue(math.isclose(list1[k], list2[k], rel_tol=tol), f"Mismatch at index {k}: {list1[k]} != {list2[k]}")
+
+    # ==========================================
+    # 3. ТЕСТЫ: BFS (Обход в ширину)
+    # ==========================================
+
+    def run_bfs_test(self, edges, n, start=0):
+        graph_c, A_s = self.build_bfs_graph(edges, n)
+        dist_c = bfs_c(start, graph_c, n)
+        v_s, _, _ = bfs_s(start, A_s)
+        idx, vals = v_s.to_lists()
+        dist_s = [BFS_INF] * n
+        for k in range(len(idx)):
+            dist_s[idx[k]] = vals[k] - 1
+        self.check_lists_equal(dist_c, dist_s)
+
+    def test_bfs_linear(self):
+        self.run_bfs_test([(0, 1), (1, 2), (2, 3), (3, 4)], n=5)
+
+    def test_bfs_star(self):
+        self.run_bfs_test([(0, 1), (0, 2), (0, 3), (0, 4)], n=5)
+
+    def test_bfs_disconnected(self):
+        self.run_bfs_test([(0, 1), (2, 3)], n=4)
+
+    def test_bfs_cycle(self):
+        self.run_bfs_test([(0, 1), (1, 2), (2, 3), (3, 0)], n=4)
+
+    def test_bfs_fully_connected(self):
+        self.run_bfs_test([(0,1), (0,2), (0,3), (1,2), (1,3), (2,3)], n=4)
+
+    # ==========================================
+    # 4. ТЕСТЫ: SSSP (Кратчайшие пути)
+    # ==========================================
+
+    def run_sssp_test(self, edges, n, start=0):
+        graph_c, A_s = self.build_sssp_graph(edges, n)
+        dist_c = sssp_c(start, graph_c, n)
+        v_s = sssp_s(start, A_s)
+        idx, vals = v_s.to_lists()
+        dist_s = [int(1e9)] * n
+        for k in range(len(idx)):
+            dist_s[idx[k]] = vals[k]
+        self.check_lists_equal(dist_c, dist_s)
+
+    def test_sssp_linear(self):
+        self.run_sssp_test([(0, 1, 5), (1, 2, 2), (2, 3, 1)], n=4)
+
+    def test_sssp_triangle_inequality(self):
+        # Путь 0->2 напрямую стоит 10, но в обход через 1 стоит 5+1=6
+        self.run_sssp_test([(0, 1, 5), (0, 2, 10), (1, 2, 1)], n=3)
+
+    def test_sssp_isolated(self):
+        self.run_sssp_test([(0, 1, 3)], n=4) # Вершины 2 и 3 изолированы
+
+    def test_sssp_two_paths(self):
+        # Два пути из 0 в 3. Верхний: 0->1->3 (вес 10+10=20). Нижний: 0->2->3 (вес 2+2=4).
+        self.run_sssp_test([(0, 1, 10), (1, 3, 10), (0, 2, 2), (2, 3, 2)], n=4)
+
+    def test_sssp_complex_graph(self):
+        edges = [(0, 1, 4), (0, 2, 1), (2, 1, 2), (1, 3, 1), (2, 3, 5), (3, 4, 3)]
+        self.run_sssp_test(edges, n=5)
+
+    # ==========================================
+    # 5. ТЕСТЫ: TC (Подсчет треугольников)
+    # ==========================================
+
+    def run_tc_test(self, edges, n):
+        graph_c, A_s = self.build_tc_graph(edges, n)
+        for i in range(n): graph_c[i].sort()
+        self.assertEqual(tc_c(graph_c, n), tc_s(A_s))
+
+    def test_tc_k4(self):
+        self.run_tc_test([(0,1), (0,2), (0,3), (1,2), (1,3), (2,3)], n=4)
+
+    def test_tc_bipartite(self):
+        self.run_tc_test([(0,1), (1,2), (2,3), (3,0)], n=4) # 0 треугольников
+
+    def test_tc_two_triangles(self):
+        self.run_tc_test([(0,1), (1,2), (2,0), (3,4), (4,5), (5,3)], n=6) # 2 треугольника
+
+    def test_tc_empty(self):
+        self.run_tc_test([], n=3) # 0 треугольников
+
+    def test_tc_one_edge(self):
+        self.run_tc_test([(0,1)], n=3) # 0 треугольников
+
+    # ==========================================
+    # 6. ТЕСТЫ: PageRank
+    # ==========================================
+
+    def run_pr_test(self, edges, n, alpha=0.85, eps=1e-5):
+        adj_in, out_degree, A_s = self.build_pr_graph(edges, n, alpha)
+        p_c, _ = pr_c(adj_in, out_degree, n, alpha, eps)
+        p_s_vec, _ = pr_s(A_s, alpha, eps)
+        idx, vals = p_s_vec.to_lists()
+        p_s = [0.0] * n
+        for k in range(len(idx)):
+            p_s[idx[k]] = vals[k]
+        self.check_lists_close(p_c, p_s, tol=1e-3)
+
+    def test_pr_star(self):
+        self.run_pr_test([(1, 0), (2, 0), (3, 0)], n=4)
+
+    def test_pr_chain(self):
+        self.run_pr_test([(0, 1), (1, 2)], n=3)
+
+    def test_pr_complete(self):
+        self.run_pr_test([(0,1), (0,2), (0,3), (1,2), (1,3), (2,3)], n=4)
+
+    def test_pr_disconnected(self):
+        self.run_pr_test([(0, 1), (2, 3)], n=4)
+
+if __name__ == '__main__':
+    unittest.main()
+c

From 102eeffe019a11300531c7eb44cc8b429ea66441 Mon Sep 17 00:00:00 2001
From: archmishka <kopevmihail7@gmail.com>
Date: Wed, 15 Jul 2026 02:36:04 +0300
Subject: [PATCH 2/4] clean result files and fix SPLA algorithms

---
 python/algorithms/bfs_result.txt          | 14824 -------------------
 python/algorithms/bfs_spla.py             |    62 +-
 python/algorithms/bfs_spla_result.txt     | 14824 -------------------
 python/algorithms/cohen_result.txt        |     2 -
 python/algorithms/fs_183_1/fs_183_1.rb    |   427 -
 python/algorithms/fs_183_1/fs_183_1.txt   |    15 -
 python/algorithms/graph.txt               |     7 +-
 python/algorithms/graph_classic.py        |    38 +-
 python/algorithms/graph_spla.py           |   181 +-
 python/algorithms/main_spla.py            |     4 +-
 python/algorithms/matrix.txt              |     3 -
 python/algorithms/pr_result.txt           | 14826 -------------------
 python/algorithms/result.txt              | 14827 --------------------
 python/algorithms/result_classic.txt      |     4 -
 python/algorithms/result_sssp_classic.txt |     4 -
 python/algorithms/sssp_spla.py            |    62 +-
 python/algorithms/test_compare.py         |    51 +-
 17 files changed, 138 insertions(+), 60023 deletions(-)
 delete mode 100644 python/algorithms/bfs_result.txt
 delete mode 100644 python/algorithms/bfs_spla_result.txt
 delete mode 100644 python/algorithms/cohen_result.txt
 delete mode 100644 python/algorithms/fs_183_1/fs_183_1.rb
 delete mode 100644 python/algorithms/fs_183_1/fs_183_1.txt
 delete mode 100644 python/algorithms/matrix.txt
 delete mode 100644 python/algorithms/pr_result.txt
 delete mode 100644 python/algorithms/result.txt
 delete mode 100644 python/algorithms/result_classic.txt
 delete mode 100644 python/algorithms/result_sssp_classic.txt

diff --git a/python/algorithms/bfs_result.txt b/python/algorithms/bfs_result.txt
deleted file mode 100644
index aba2ac14a..000000000
--- a/python/algorithms/bfs_result.txt
+++ /dev/null
@@ -1,14824 +0,0 @@
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diff --git a/python/algorithms/bfs_spla.py b/python/algorithms/bfs_spla.py
index de3ead706..952f15705 100644
--- a/python/algorithms/bfs_spla.py
+++ b/python/algorithms/bfs_spla.py
@@ -1,6 +1,3 @@
-import sys
-import argparse
-from pathlib import Path
 from pyspla import *
 
 def bfs(s: int, A: Matrix):
@@ -17,61 +14,4 @@ def bfs(s: int, A: Matrix):
         front = front.vxm(v, A, op_mult=INT.LAND, op_add=INT.LOR, op_select=INT.EQZERO)
         front_size = front.reduce(op_reduce=INT.PLUS).get()
 
-    return v, count, depth.get()
-
-def read_vectors(filename):
-    with open(filename) as f:
-        I = list(map(int, f.readline().replace(',', ' ').split()))
-        J = list(map(int, f.readline().replace(',', ' ').split()))
-        V = list(map(int, f.readline().replace(',', ' ').split()))
-    rows = int(max(I) + 1)
-    cols = int(max(J) + 1)
-    return Matrix.from_lists(I, J, V, shape=(rows, cols), dtype=INT)
-
-def read_mtx(filename):
-    I, J, V = [], [], []
-    n_vertices = 0
-    with open(filename, 'r') as f:
-        for line in f:
-            if line.startswith('%'):
-                continue
-            parts = line.split()
-            if n_vertices == 0:
-                n_vertices = int(parts[0])
-                continue
-            i = int(parts[0]) - 1
-            j = int(parts[1]) - 1
-            I.append(i); J.append(j); V.append(1)
-            if i != j:
-                I.append(j); J.append(i); V.append(1)
-    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=INT)
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-m", "--matrix", type=Path)
-    parser.add_argument("-v", "--vectors", type=Path)
-    parser.add_argument("-s", "--start", type=int, default=0)
-    parser.add_argument("-o", "--output", type=Path, default="bfs_spla_result.txt")
-    args = parser.parse_args()
-
-    if args.matrix and args.matrix.exists():
-        A = read_mtx(str(args.matrix))
-    elif args.vectors and args.vectors.exists():
-        A = read_vectors(str(args.vectors))
-    else:
-        sys.exit(1)
-
-    v, count, depth = bfs(args.start, A)
-
-    with open(args.output, 'w') as f_out:
-        f_out.write(f"Reached vertices: {count}\n")
-        f_out.write(f"Max depth: {depth - 1}\n")
-        indices, values = v.to_lists()
-        for i, dist in zip(indices, values):
-            f_out.write(f"{i} {dist - 1}\n")
-
-    print(f"Result written to {args.output}")
-
-if __name__ == "__main__":
-    import os
-    main()
\ No newline at end of file
+    return v, count, depth.get()
\ No newline at end of file
diff --git a/python/algorithms/bfs_spla_result.txt b/python/algorithms/bfs_spla_result.txt
deleted file mode 100644
index aba2ac14a..000000000
--- a/python/algorithms/bfs_spla_result.txt
+++ /dev/null
@@ -1,14824 +0,0 @@
-Reached vertices: 14822
-Max depth: 46
-0 0
-1 1
-2 1
-3 1
-4 1
-5 1
-6 1
-7 1
-8 1
-9 1
-10 1
-11 1
-12 1
-13 1
-14 1
-15 1
-16 1
-17 1
-18 1
-19 1
-20 3
-21 4
-22 3
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-25 2
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-31 2
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-33 2
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-52 1
-53 1
-54 1
-55 1
-56 1
-57 1
-58 1
-59 1
-60 3
-61 3
-62 2
-63 2
-64 2
-65 2
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-71 3
-72 4
-73 4
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-76 3
-77 3
-78 3
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-81 2
-82 2
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diff --git a/python/algorithms/cohen_result.txt b/python/algorithms/cohen_result.txt
deleted file mode 100644
index 81a98f9a1..000000000
--- a/python/algorithms/cohen_result.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-Matrix: Pres_Poisson.mtx
-Triangles: 3229525
diff --git a/python/algorithms/fs_183_1/fs_183_1.rb b/python/algorithms/fs_183_1/fs_183_1.rb
deleted file mode 100644
index 0210c33de..000000000
--- a/python/algorithms/fs_183_1/fs_183_1.rb
+++ /dev/null
@@ -1,427 +0,0 @@
-HB/fs_183_1; 1983; A. Curtis; ed: I. Duff et al.                       |141     
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diff --git a/python/algorithms/fs_183_1/fs_183_1.txt b/python/algorithms/fs_183_1/fs_183_1.txt
deleted file mode 100644
index e55f31d71..000000000
--- a/python/algorithms/fs_183_1/fs_183_1.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-%-------------------------------------------------------------------------------
-% UF Sparse Matrix Collection, Tim Davis
-% http://www.cise.ufl.edu/research/sparse/matrices/HB/fs_183_1
-% name: HB/fs_183_1
-% [UNSYMMETRIC FACSIMILE CONVERGENCE MATRIX]
-% id: 141
-% date: 1983
-% author: A. Curtis
-% ed: I. Duff, R. Grimes, J. Lewis
-% fields: title A Zeros name id kind notes date author ed
-% kind: 2D/3D problem sequence
-%-------------------------------------------------------------------------------
-% notes:
-% next: HB/fs_183_3 first: HB/fs_183_1
-%-------------------------------------------------------------------------------
diff --git a/python/algorithms/graph.txt b/python/algorithms/graph.txt
index bceed69ff..6a811d94e 100644
--- a/python/algorithms/graph.txt
+++ b/python/algorithms/graph.txt
@@ -1,3 +1,4 @@
-0 0 1 1 2 2 2 3
-1 2 0 2 0 1 3 2
-1 1 1 1 1 1 1 1
+3
+0 1 2
+1 2 0
+5 3 2
diff --git a/python/algorithms/graph_classic.py b/python/algorithms/graph_classic.py
index ce66053de..1096d47a3 100644
--- a/python/algorithms/graph_classic.py
+++ b/python/algorithms/graph_classic.py
@@ -20,19 +20,19 @@ def read_mtx_unweighted(filename):
 
 def read_vectors_unweighted(filename):
     with open(filename, 'r') as f:
-        I = list(map(int, f.readline().replace(',', ' ').split()))
-        J = list(map(int, f.readline().replace(',', ' ').split()))
-        
-    n = max(max(I), max(J)) + 1 if I else 0
+        n_line = f.readline().strip()
+        if not n_line:
+            return defaultdict(list), 0
+        n = int(n_line)
+        I = list(map(int, f.readline().split()))
+        J = list(map(int, f.readline().split()))
     graph = defaultdict(list)
-    
     for k in range(len(I)):
         i = I[k]
         j = J[k]
         graph[i].append(j)
         if i != j:
             graph[j].append(i)
-            
     return graph, n
 
 def read_mtx_weighted(filename):
@@ -56,13 +56,14 @@ def read_mtx_weighted(filename):
 
 def read_vectors_weighted(filename):
     with open(filename, 'r') as f:
-        I = list(map(int, f.readline().replace(',', ' ').split()))
-        J = list(map(int, f.readline().replace(',', ' ').split()))
-        V = list(map(float, f.readline().replace(',', ' ').split()))
-        
-    n = max(max(I), max(J)) + 1 if I else 0
+        n_line = f.readline().strip()
+        if not n_line:
+            return defaultdict(list), 0
+        n = int(n_line)
+        I = list(map(int, f.readline().split()))
+        J = list(map(int, f.readline().split()))
+        V = list(map(float, f.readline().split()))
     graph = defaultdict(list)
-    
     for k in range(len(I)):
         i = I[k]
         j = J[k]
@@ -70,7 +71,6 @@ def read_vectors_weighted(filename):
         graph[i].append((j, v))
         if i != j:
             graph[j].append((i, v))
-            
     return graph, n
 
 def read_mtx_pr_classic(filename):
@@ -96,13 +96,14 @@ def read_mtx_pr_classic(filename):
 
 def read_vectors_pr_classic(filename):
     with open(filename, 'r') as f:
-        I = list(map(int, f.readline().replace(',', ' ').split()))
-        J = list(map(int, f.readline().replace(',', ' ').split()))
-        
-    n = max(max(I), max(J)) + 1 if I else 0
+        n_line = f.readline().strip()
+        if not n_line:
+            return defaultdict(list), [], 0
+        n = int(n_line)
+        I = list(map(int, f.readline().split()))
+        J = list(map(int, f.readline().split()))
     adj_in = defaultdict(list)
     out_degree = [0] * n
-    
     for k in range(len(I)):
         i = I[k]
         j = J[k]
@@ -111,5 +112,4 @@ def read_vectors_pr_classic(filename):
         if i != j:
             adj_in[i].append(j)
             out_degree[j] += 1
-            
     return adj_in, out_degree, n
\ No newline at end of file
diff --git a/python/algorithms/graph_spla.py b/python/algorithms/graph_spla.py
index 5f9f19a1b..39883b935 100644
--- a/python/algorithms/graph_spla.py
+++ b/python/algorithms/graph_spla.py
@@ -1,164 +1,117 @@
 from pyspla import *
 
 def read_mtx_int(filename):
-    I, J, V = [], [], []
-    n_vertices = 0
-    
+    row_indices, col_indices, values = [], [], []
+    n = 0
     with open(filename, 'r') as f:
         for line in f:
             if line.startswith('%'):
                 continue
             parts = line.split()
-            if n_vertices == 0:
-                n_vertices = int(parts[0])
+            if n == 0:
+                n = int(parts[0])
                 continue
-                
             i = int(parts[0]) - 1
             j = int(parts[1]) - 1
-            
-            I.append(i)
-            J.append(j)
-            V.append(1)
-            
+            row_indices.append(i)
+            col_indices.append(j)
+            values.append(1)
             if i != j:
-                I.append(j)
-                J.append(i)
-                V.append(1)
-                
-    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=INT)
+                row_indices.append(j)
+                col_indices.append(i)
+                values.append(1)
+    return Matrix.from_lists(row_indices, col_indices, values, shape=(n, n), dtype=INT)
 
 def read_vectors_int(filename):
     with open(filename, 'r') as f:
-        raw_I = list(map(int, f.readline().replace(',', ' ').split()))
-        raw_J = list(map(int, f.readline().replace(',', ' ').split()))
-        
-    n_vertices = max(max(raw_I), max(raw_J)) + 1
-    I, J, V = [], [], []
-    
-    for k in range(len(raw_I)):
-        i = raw_I[k]
-        j = raw_J[k]
-        
-        I.append(i)
-        J.append(j)
-        V.append(1)
-        
+        n_line = f.readline().strip()
+        if not n_line:
+            return Matrix.from_lists([], [], [], shape=(0,0), dtype=INT)
+        n = int(n_line)
+        rows = list(map(int, f.readline().split()))
+        cols = list(map(int, f.readline().split()))
+    row_indices, col_indices, values = [], [], []
+    for k in range(len(rows)):
+        i = rows[k]; j = cols[k]
+        row_indices.append(i); col_indices.append(j); values.append(1)
         if i != j:
-            I.append(j)
-            J.append(i)
-            V.append(1)
-            
-    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=INT)
+            row_indices.append(j); col_indices.append(i); values.append(1)
+    return Matrix.from_lists(row_indices, col_indices, values, shape=(n, n), dtype=INT)
 
 def read_mtx_float(filename):
-    I, J, V = [], [], []
-    n_vertices = 0
-    
+    row_indices, col_indices, values = [], [], []
+    n = 0
     with open(filename, 'r') as f:
         for line in f:
             if line.startswith('%'):
                 continue
             parts = line.split()
-            if n_vertices == 0:
-                n_vertices = int(parts[0])
+            if n == 0:
+                n = int(parts[0])
                 continue
-                
             i = int(parts[0]) - 1
             j = int(parts[1]) - 1
-            weight = float(parts[2]) if len(parts) > 2 else 1.0
-            
-            I.append(i)
-            J.append(j)
-            V.append(weight)
-            
+            w = float(parts[2]) if len(parts) > 2 else 1.0
+            row_indices.append(i); col_indices.append(j); values.append(w)
             if i != j:
-                I.append(j)
-                J.append(i)
-                V.append(weight)
-                
-    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=FLOAT)
+                row_indices.append(j); col_indices.append(i); values.append(w)
+    return Matrix.from_lists(row_indices, col_indices, values, shape=(n, n), dtype=FLOAT)
 
 def read_vectors_float(filename):
     with open(filename, 'r') as f:
-        raw_I = list(map(int, f.readline().replace(',', ' ').split()))
-        raw_J = list(map(int, f.readline().replace(',', ' ').split()))
-        raw_V = list(map(float, f.readline().replace(',', ' ').split()))
-        
-    n_vertices = max(max(raw_I), max(raw_J)) + 1
-    I, J, V = [], [], []
-    
-    for k in range(len(raw_I)):
-        i = raw_I[k]
-        j = raw_J[k]
-        weight = raw_V[k]
-        
-        I.append(i)
-        J.append(j)
-        V.append(weight)
-        
+        n_line = f.readline().strip()
+        if not n_line:
+            return Matrix.from_lists([], [], [], shape=(0,0), dtype=FLOAT)
+        n = int(n_line)
+        rows = list(map(int, f.readline().split()))
+        cols = list(map(int, f.readline().split()))
+        weights = list(map(float, f.readline().split()))
+    row_indices, col_indices, values = [], [], []
+    for k in range(len(rows)):
+        i = rows[k]; j = cols[k]; w = weights[k]
+        row_indices.append(i); col_indices.append(j); values.append(w)
         if i != j:
-            I.append(j)
-            J.append(i)
-            V.append(weight)
-            
-    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=FLOAT)
+            row_indices.append(j); col_indices.append(i); values.append(w)
+    return Matrix.from_lists(row_indices, col_indices, values, shape=(n, n), dtype=FLOAT)
 
 def read_mtx_pr(filename, alpha):
-    I, J, V = [], [], []
-    n_vertices = 0
-    
+    row_indices, col_indices = [], []
+    n = 0
     with open(filename, 'r') as f:
         for line in f:
             if line.startswith('%'):
                 continue
             parts = line.split()
-            if n_vertices == 0:
-                n_vertices = int(parts[0])
-                out_degree = [0] * n_vertices
+            if n == 0:
+                n = int(parts[0])
+                out_degree = [0] * n
                 continue
-                
             i = int(parts[0]) - 1
             j = int(parts[1]) - 1
-            
-            I.append(i)
-            J.append(j)
+            row_indices.append(i); col_indices.append(j)
             out_degree[i] += 1
-            
             if i != j:
-                I.append(j)
-                J.append(i)
+                row_indices.append(j); col_indices.append(i)
                 out_degree[j] += 1
-                
-    for k in range(len(I)):
-        u = I[k]
-        V.append(alpha / out_degree[u])
-        
-    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=FLOAT)
+    values = [alpha / out_degree[u] for u in row_indices]
+    return Matrix.from_lists(row_indices, col_indices, values, shape=(n, n), dtype=FLOAT)
 
 def read_vectors_pr(filename, alpha):
     with open(filename, 'r') as f:
-        raw_I = list(map(int, f.readline().replace(',', ' ').split()))
-        raw_J = list(map(int, f.readline().replace(',', ' ').split()))
-        
-    n_vertices = max(max(raw_I), max(raw_J)) + 1
-    out_degree = [0] * n_vertices
-    I, J, V = [], [], []
-    
-    for k in range(len(raw_I)):
-        i = raw_I[k]
-        j = raw_J[k]
-        
-        I.append(i)
-        J.append(j)
+        n_line = f.readline().strip()
+        if not n_line:
+            return Matrix.from_lists([], [], [], shape=(0,0), dtype=FLOAT)
+        n = int(n_line)
+        rows = list(map(int, f.readline().split()))
+        cols = list(map(int, f.readline().split()))
+    out_degree = [0] * n
+    row_indices, col_indices = [], []
+    for k in range(len(rows)):
+        i = rows[k]; j = cols[k]
+        row_indices.append(i); col_indices.append(j)
         out_degree[i] += 1
-        
         if i != j:
-            I.append(j)
-            J.append(i)
+            row_indices.append(j); col_indices.append(i)
             out_degree[j] += 1
-            
-    for k in range(len(I)):
-        u = I[k]
-        V.append(alpha / out_degree[u])
-            
-    return Matrix.from_lists(I, J, V, shape=(n_vertices, n_vertices), dtype=FLOAT)
\ No newline at end of file
+    values = [alpha / out_degree[u] for u in row_indices]
+    return Matrix.from_lists(row_indices, col_indices, values, shape=(n, n), dtype=FLOAT)
\ No newline at end of file
diff --git a/python/algorithms/main_spla.py b/python/algorithms/main_spla.py
index 8c1d38dfc..15eb82e7e 100644
--- a/python/algorithms/main_spla.py
+++ b/python/algorithms/main_spla.py
@@ -3,7 +3,7 @@
 from pathlib import Path
 
 from bfs_spla import bfs
-from sssp_spla import sssp, INF
+from sssp_spla import sssp_spla, INF
 from pr_spla import pagerank
 from tc_spla import cohen
 
@@ -44,7 +44,7 @@ def main():
             A = read_mtx_float(str(args.matrix))
         else:
             A = read_vectors_float(str(args.vectors))
-        v = sssp(args.start, A)
+        v = sssp_spla(args.start, A)
         with open(args.output, 'w') as f_out:
             idx, vals = v.to_lists()
             for k in range(len(idx)):
diff --git a/python/algorithms/matrix.txt b/python/algorithms/matrix.txt
deleted file mode 100644
index 5b2597053..000000000
--- a/python/algorithms/matrix.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-0 1 2 2 3
-1 2 0 3 2
-1 1 1 1 1
diff --git a/python/algorithms/pr_result.txt b/python/algorithms/pr_result.txt
deleted file mode 100644
index ce8be4b1f..000000000
--- a/python/algorithms/pr_result.txt
+++ /dev/null
@@ -1,14826 +0,0 @@
---- PageRank (Pres_Poisson.mtx) ---
-alpha=0.85, eps=0.0001
-Iterations: 2
-Vertex | Rank
-0 0.000010
-1 0.000010
-2 0.000010
-3 0.000010
-4 0.000010
-5 0.000010
-6 0.000010
-7 0.000010
-8 0.000010
-9 0.000010
-10 0.000010
-11 0.000010
-12 0.000010
-13 0.000010
-14 0.000010
-15 0.000010
-16 0.000010
-17 0.000010
-18 0.000010
-19 0.000010
-20 0.000010
-21 0.000010
-22 0.000010
-23 0.000010
-24 0.000010
-25 0.000010
-26 0.000010
-27 0.000010
-28 0.000010
-29 0.000010
-30 0.000010
-31 0.000010
-32 0.000010
-33 0.000010
-34 0.000010
-35 0.000010
-36 0.000010
-37 0.000010
-38 0.000010
-39 0.000010
-40 0.000010
-41 0.000010
-42 0.000010
-43 0.000010
-44 0.000010
-45 0.000010
-46 0.000010
-47 0.000010
-48 0.000010
-49 0.000010
-50 0.000010
-51 0.000010
-52 0.000010
-53 0.000010
-54 0.000010
-55 0.000010
-56 0.000010
-57 0.000010
-58 0.000010
-59 0.000010
-60 0.000010
-61 0.000010
-62 0.000010
-63 0.000010
-64 0.000010
-65 0.000010
-66 0.000010
-67 0.000010
-68 0.000010
-69 0.000010
-70 0.000010
-71 0.000010
-72 0.000010
-73 0.000010
-74 0.000010
-75 0.000010
-76 0.000010
-77 0.000010
-78 0.000010
-79 0.000010
-80 0.000010
-81 0.000010
-82 0.000010
-83 0.000010
-84 0.000010
-85 0.000010
-86 0.000010
-87 0.000010
-88 0.000010
-89 0.000010
-90 0.000010
-91 0.000010
-92 0.000010
-93 0.000010
-94 0.000010
-95 0.000010
-96 0.000010
-97 0.000010
-98 0.000010
-99 0.000010
-100 0.000010
-101 0.000010
-102 0.000010
-103 0.000010
-104 0.000010
-105 0.000010
-106 0.000010
-107 0.000010
-108 0.000010
-109 0.000010
-110 0.000010
-111 0.000010
-112 0.000010
-113 0.000010
-114 0.000010
-115 0.000010
-116 0.000010
-117 0.000010
-118 0.000010
-119 0.000010
-120 0.000010
-121 0.000010
-122 0.000010
-123 0.000010
-124 0.000010
-125 0.000010
-126 0.000010
-127 0.000010
-128 0.000010
-129 0.000010
-130 0.000010
-131 0.000010
-132 0.000010
-133 0.000010
-134 0.000010
-135 0.000010
-136 0.000010
-137 0.000010
-138 0.000010
-139 0.000010
-140 0.000010
-141 0.000010
-142 0.000010
-143 0.000010
-144 0.000010
-145 0.000010
-146 0.000010
-147 0.000010
-148 0.000010
-149 0.000010
-150 0.000010
-151 0.000010
-152 0.000010
-153 0.000010
-154 0.000010
-155 0.000010
-156 0.000010
-157 0.000010
-158 0.000010
-159 0.000010
-160 0.000010
-161 0.000010
-162 0.000010
-163 0.000010
-164 0.000010
-165 0.000010
-166 0.000010
-167 0.000010
-168 0.000010
-169 0.000010
-170 0.000010
-171 0.000010
-172 0.000010
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-174 0.000010
-175 0.000010
-176 0.000010
-177 0.000010
-178 0.000010
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-212 0.000010
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-215 0.000010
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-217 0.000010
-218 0.000010
-219 0.000010
-220 0.000010
-221 0.000010
-222 0.000010
-223 0.000010
-224 0.000010
-225 0.000010
-226 0.000010
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-230 0.000010
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-314 0.000010
-315 0.000010
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-317 0.000010
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-350 0.000010
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-360 0.000010
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-373 0.000010
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-380 0.000010
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-383 0.000010
-384 0.000010
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-423 0.000010
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-430 0.000010
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diff --git a/python/algorithms/result.txt b/python/algorithms/result.txt
deleted file mode 100644
index e11bebdec..000000000
--- a/python/algorithms/result.txt
+++ /dev/null
@@ -1,14827 +0,0 @@
---- BFS results for matrix Pres_Poisson.mtx ---
-Reached vertices: 14822
-Max depth: 46
-----------------------------------------
-Vertex | Distance
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-1 2
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-3 2
-4 2
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diff --git a/python/algorithms/result_classic.txt b/python/algorithms/result_classic.txt
deleted file mode 100644
index d7779cac3..000000000
--- a/python/algorithms/result_classic.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-0 0.0
-1 1.0
-2 1.0
-3 2.0
diff --git a/python/algorithms/result_sssp_classic.txt b/python/algorithms/result_sssp_classic.txt
deleted file mode 100644
index f51c640f8..000000000
--- a/python/algorithms/result_sssp_classic.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-0 0
-1 1.0
-2 1.0
-3 2.0
diff --git a/python/algorithms/sssp_spla.py b/python/algorithms/sssp_spla.py
index af1a54211..bbc886d98 100644
--- a/python/algorithms/sssp_spla.py
+++ b/python/algorithms/sssp_spla.py
@@ -1,30 +1,38 @@
-from pyspla import Matrix, Vector, FLOAT, Scalar
+from pyspla import *
 
-INF = 1e9
+INF = float(1e9)
 
-def sssp_broken(start: int, A: Matrix):
-    N = A.n_rows
-    v = Vector(N, FLOAT)
-    v.set(start, 0.0)
-    
-    frontier = Vector(N, FLOAT)
-    frontier.set(start, 0.0)
-    
-    dummy_mask = Vector.dense(N, FLOAT, 1.0)
+def sssp_spla(start: int, A: Matrix):
+    n = A.n_rows
+    initial_indices = list(range(n))
+    initial_values = [INF] * n
+    initial_values[start] = 0.0
+    dist = Vector.from_lists(initial_indices, initial_values, n, FLOAT)
+
+    mask = Vector.dense(n, FLOAT, 1.0)
     inf_scalar = Scalar(FLOAT, INF)
-    
-    step = 0
-    front_size = 1 
-    while front_size > 0 and step < N:
-        frontier = frontier.vxm(dummy_mask, A, op_mult=FLOAT.PLUS, op_add=FLOAT.MIN, op_select=FLOAT.ALWAYS, init=inf_scalar)
-        v = v.eadd(FLOAT.MIN, frontier)
-        idx, vals = v.to_lists()
-        print(f"Iteration {step} V: {sorted(list(zip(idx, vals)))}")
-        idx2, vals2 = frontier.to_lists()
-        print(f"Iteration {step} Frontier: {sorted(list(zip(idx2, vals2)))}")
-        front_size = frontier.reduce(FLOAT.PLUS).get()
-        print(f"Iteration {step} Frontier sum: {front_size}")
-        print('-' * 24)
-        step += 1
-    
-    return v
\ No newline at end of file
+
+    for iteration in range(n - 1):
+        new_dist = dist.vxm(mask, A,
+                            op_mult=FLOAT.PLUS,
+                            op_add=FLOAT.MIN,
+                            op_select=FLOAT.ALWAYS,
+                            init=inf_scalar)
+        relaxed = dist.eadd(FLOAT.MIN, new_dist)
+        indices, values = relaxed.to_lists()
+        if start not in indices:
+            indices.append(start)
+            values.append(0.0)
+        dist = Vector.from_lists(indices, values, n, FLOAT)
+
+    final_indices, final_values = dist.to_lists()
+    final_map = {i: INF for i in range(n)}
+    for k in range(len(final_indices)):
+        val = final_values[k]
+        if val == 0.0 and final_indices[k] != start:
+            val = INF
+        final_map[final_indices[k]] = val
+
+    all_indices = list(range(n))
+    all_values = [final_map[i] for i in all_indices]
+    return Vector.from_lists(all_indices, all_values, n, FLOAT)
\ No newline at end of file
diff --git a/python/algorithms/test_compare.py b/python/algorithms/test_compare.py
index 2cd814966..13920f998 100644
--- a/python/algorithms/test_compare.py
+++ b/python/algorithms/test_compare.py
@@ -9,16 +9,12 @@
 from tc_classic import tc_simple as tc_c
 
 from bfs_spla import bfs as bfs_s
-from sssp_spla import sssp as sssp_s
+from sssp_spla import sssp_spla as sssp_s
 from pr_spla import pagerank as pr_s
 from tc_spla import cohen as tc_s
 
 class TestCompareAlgorithms(unittest.TestCase):
 
-    # ==========================================
-    # 1. ПРОСТЫЕ ФУНКЦИИ СОЗДАНИЯ ГРАФОВ
-    # ==========================================
-
     def build_bfs_graph(self, edges, n):
         graph_c = defaultdict(list)
         I, J, V = [], [], []
@@ -39,7 +35,7 @@ def build_sssp_graph(self, edges, n):
             I.extend([u, v])
             J.extend([v, u])
             V.extend([w, w])
-        return graph_c, Matrix.from_lists(I, J, V, shape=(n, n), dtype=INT)
+        return graph_c, Matrix.from_lists(I, J, V, shape=(n, n), dtype=FLOAT)
 
     def build_tc_graph(self, edges, n):
         graph_c = defaultdict(list)
@@ -56,27 +52,18 @@ def build_pr_graph(self, edges, n, alpha=0.85):
         adj_in = defaultdict(list)
         out_degree = [0] * n
         I, J, V = [], [], []
-        
-        # Сначала считаем исходящие ребра
         for u, v in edges:
             out_degree[u] += 1
             if u != v:
                 out_degree[v] += 1
-                
-        # Затем строим графы
         for u, v in edges:
             adj_in[v].append(u)
             I.append(u); J.append(v); V.append(alpha / out_degree[u])
             if u != v:
                 adj_in[u].append(v)
                 I.append(v); J.append(u); V.append(alpha / out_degree[v])
-                
         return adj_in, out_degree, Matrix.from_lists(I, J, V, shape=(n, n), dtype=FLOAT)
 
-    # ==========================================
-    # 2. ФУНКЦИИ СРАВНЕНИЯ РЕЗУЛЬТАТОВ
-    # ==========================================
-
     def check_lists_equal(self, list1, list2):
         self.assertEqual(len(list1), len(list2))
         for k in range(len(list1)):
@@ -87,10 +74,6 @@ def check_lists_close(self, list1, list2, tol=1e-3):
         for k in range(len(list1)):
             self.assertTrue(math.isclose(list1[k], list2[k], rel_tol=tol), f"Mismatch at index {k}: {list1[k]} != {list2[k]}")
 
-    # ==========================================
-    # 3. ТЕСТЫ: BFS (Обход в ширину)
-    # ==========================================
-
     def run_bfs_test(self, edges, n, start=0):
         graph_c, A_s = self.build_bfs_graph(edges, n)
         dist_c = bfs_c(start, graph_c, n)
@@ -116,10 +99,6 @@ def test_bfs_cycle(self):
     def test_bfs_fully_connected(self):
         self.run_bfs_test([(0,1), (0,2), (0,3), (1,2), (1,3), (2,3)], n=4)
 
-    # ==========================================
-    # 4. ТЕСТЫ: SSSP (Кратчайшие пути)
-    # ==========================================
-
     def run_sssp_test(self, edges, n, start=0):
         graph_c, A_s = self.build_sssp_graph(edges, n)
         dist_c = sssp_c(start, graph_c, n)
@@ -134,47 +113,38 @@ def test_sssp_linear(self):
         self.run_sssp_test([(0, 1, 5), (1, 2, 2), (2, 3, 1)], n=4)
 
     def test_sssp_triangle_inequality(self):
-        # Путь 0->2 напрямую стоит 10, но в обход через 1 стоит 5+1=6
         self.run_sssp_test([(0, 1, 5), (0, 2, 10), (1, 2, 1)], n=3)
 
     def test_sssp_isolated(self):
-        self.run_sssp_test([(0, 1, 3)], n=4) # Вершины 2 и 3 изолированы
+        self.run_sssp_test([(0, 1, 3)], n=4)
 
     def test_sssp_two_paths(self):
-        # Два пути из 0 в 3. Верхний: 0->1->3 (вес 10+10=20). Нижний: 0->2->3 (вес 2+2=4).
         self.run_sssp_test([(0, 1, 10), (1, 3, 10), (0, 2, 2), (2, 3, 2)], n=4)
 
     def test_sssp_complex_graph(self):
         edges = [(0, 1, 4), (0, 2, 1), (2, 1, 2), (1, 3, 1), (2, 3, 5), (3, 4, 3)]
         self.run_sssp_test(edges, n=5)
 
-    # ==========================================
-    # 5. ТЕСТЫ: TC (Подсчет треугольников)
-    # ==========================================
-
     def run_tc_test(self, edges, n):
         graph_c, A_s = self.build_tc_graph(edges, n)
-        for i in range(n): graph_c[i].sort()
+        for i in range(n):
+            graph_c[i].sort()
         self.assertEqual(tc_c(graph_c, n), tc_s(A_s))
 
     def test_tc_k4(self):
         self.run_tc_test([(0,1), (0,2), (0,3), (1,2), (1,3), (2,3)], n=4)
 
     def test_tc_bipartite(self):
-        self.run_tc_test([(0,1), (1,2), (2,3), (3,0)], n=4) # 0 треугольников
+        self.run_tc_test([(0,1), (1,2), (2,3), (3,0)], n=4)
 
     def test_tc_two_triangles(self):
-        self.run_tc_test([(0,1), (1,2), (2,0), (3,4), (4,5), (5,3)], n=6) # 2 треугольника
+        self.run_tc_test([(0,1), (1,2), (2,0), (3,4), (4,5), (5,3)], n=6)
 
     def test_tc_empty(self):
-        self.run_tc_test([], n=3) # 0 треугольников
+        self.run_tc_test([], n=3)
 
     def test_tc_one_edge(self):
-        self.run_tc_test([(0,1)], n=3) # 0 треугольников
-
-    # ==========================================
-    # 6. ТЕСТЫ: PageRank
-    # ==========================================
+        self.run_tc_test([(0,1)], n=3)
 
     def run_pr_test(self, edges, n, alpha=0.85, eps=1e-5):
         adj_in, out_degree, A_s = self.build_pr_graph(edges, n, alpha)
@@ -199,5 +169,4 @@ def test_pr_disconnected(self):
         self.run_pr_test([(0, 1), (2, 3)], n=4)
 
 if __name__ == '__main__':
-    unittest.main()
-c
+    unittest.main()
\ No newline at end of file

From 0886e3a37f335bcd90cfeae8f6c0c5cde28e831b Mon Sep 17 00:00:00 2001
From: archmishka <kopevmihail7@gmail.com>
Date: Wed, 15 Jul 2026 16:03:31 +0300
Subject: [PATCH 3/4] Add README.md

---
 python/algorithms/README.md | 84 +++++++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 python/algorithms/README.md

diff --git a/python/algorithms/README.md b/python/algorithms/README.md
new file mode 100644
index 000000000..18b863f3d
--- /dev/null
+++ b/python/algorithms/README.md
@@ -0,0 +1,84 @@
+# Graph Algorithms with SPLA
+
+This project provides two implementations of graph algorithms:
+
+- **Classic (CPU)**: naive implementation in Python using adjacency lists.
+- **SPLA (GPU)**: implementation using sparse linear algebra primitives from the SPLA library, targeting GPU execution.
+
+Tests (`test_compare.py`) verify that both versions produce identical results.
+
+---
+
+## Supported Algorithms
+
+| Algorithm | Flag |
+|-----------|------|
+| BFS (Breadth-First Search) | `--algo bfs` |
+| SSSP (Single-Source Shortest Paths) | `--algo sssp` |
+| PageRank | `--algo pr` |
+| Triangle Counting | `--algo tc` |
+
+---
+
+## Installation
+ Create and activate a virtual environment:
+
+```bash
+cd python
+python -m venv venv
+source venv/bin/activate 
+cd algorithms
+```
+## Usage
+
+Two entry points are provided:
+
+- `main_spla.py` – GPU-accelerated version (SPLA)
+- `main_classic.py` – CPU reference version
+
+Both scripts accept the same command-line arguments.
+
+### Command Syntax
+
+```bash
+python main_spla.py --algo <bfs|sssp|pr|tc> (-m <file.mtx> | -v <file.txt>) [options]
+```
+
+### Required Arguments
+
+| Argument | Description |
+|----------|-------------|
+| `--algo` | Algorithm to run: `bfs`, `sssp`, `pr`, `tc` |
+| `-m` or `-v` | Input graph file. Use `-m` for Matrix Market (`.mtx`) or `-v` for vectors format (`.txt`). |
+
+### Optional Arguments
+
+| Option | Default | Description |
+|--------|---------|-------------|
+| `-o` | `result.txt` | Output file path |
+| `-s` | `0` | Start vertex (for BFS and SSSP) |
+| `-a` | `0.85` | Damping factor (for PageRank) |
+| `-e` | `1e-4` | Convergence tolerance (for PageRank) |
+| `-h`, `--help` | – | Show help message and exit |
+
+---
+
+## Examples
+
+```bash
+# BFS on a Matrix Market file
+python main_spla.py --algo bfs -m graph.mtx -s 0 -o bfs_result.txt
+
+# SSSP on a vectors file (weighted graph)
+python main_spla.py --algo sssp -v weighted.txt -s 0 -o sssp_result.txt
+
+# PageRank with custom parameters
+python main_spla.py --algo pr -v graph.txt -a 0.85 -e 1e-6 -o pr_result.txt
+
+# Triangle Counting
+python main_spla.py --algo tc -m graph.mtx -o tc_result.txt
+
+# Classic reference version
+python main_classic.py --algo bfs -v graph.txt -s 0 -o bfs_classic.txt
+```
+

From 2ac847ae31e49c9dc3829dccf8a4af1a0dbc8941 Mon Sep 17 00:00:00 2001
From: archmishka <kopevmihail7@gmail.com>
Date: Wed, 15 Jul 2026 23:49:31 +0300
Subject: [PATCH 4/4] Fix README.md

---
 python/algorithms/README.md | 115 ++++++++++++++++++++++++------------
 1 file changed, 77 insertions(+), 38 deletions(-)

diff --git a/python/algorithms/README.md b/python/algorithms/README.md
index 18b863f3d..997e351ea 100644
--- a/python/algorithms/README.md
+++ b/python/algorithms/README.md
@@ -1,12 +1,16 @@
 # Graph Algorithms with SPLA
 
-This project provides two implementations of graph algorithms:
+This project contains two implementations of graph algorithms:
 
-- **Classic (CPU)**: naive implementation in Python using adjacency lists.
-- **SPLA (GPU)**: implementation using sparse linear algebra primitives from the SPLA library, targeting GPU execution.
+- **Classic (CPU):** Python reference implementation
+- **SPLA (GPU):** implementation using sparse linear algebra primitives from SPLA
 
-Tests (`test_compare.py`) verify that both versions produce identical results.
+Tests (`test_compare.py`) checks that both implementations produce identical results.
 
+Run tests:
+```bash 
+python -m unittest test_compare.py -v
+```
 ---
 
 ## Supported Algorithms
@@ -21,58 +25,62 @@ Tests (`test_compare.py`) verify that both versions produce identical results.
 ---
 
 ## Installation
- Create and activate a virtual environment:
 
 ```bash
 cd python
 python -m venv venv
-source venv/bin/activate 
+source venv/bin/activate      
+pip install -e .
 cd algorithms
 ```
-## Usage
-
-Two entry points are provided:
 
-- `main_spla.py` – GPU-accelerated version (SPLA)
-- `main_classic.py` – CPU reference version
-
-Both scripts accept the same command-line arguments.
+---
 
-### Command Syntax
+## Usage
 
-```bash
-python main_spla.py --algo <bfs|sssp|pr|tc> (-m <file.mtx> | -v <file.txt>) [options]
+Two entry points are available:
+
+- `main_spla.py` — GPU version (SPLA)
+- `main_classic.py` — CPU reference version
+
+Both scripts use the same CLI arguments.
+
+```text
+usage: main_spla.py [-h] --algo {bfs,sssp,pr,tc} [-m MATRIX] [-v VECTORS] [-o OUTPUT] [-s START] [-a ALPHA] [-e EPS]
+
+options:
+  -h, --help            show this help message and exit
+  --algo {bfs,sssp,pr,tc}
+                        Algorithm to run:
+                        bfs  - Breadth-First Search
+                        sssp - Single-Source Shortest Paths
+                        pr   - PageRank
+                        tc   - Triangle Counting
+  -m MATRIX, --matrix MATRIX
+                        Path to graph in Matrix Market format (.mtx)
+  -v VECTORS, --vectors VECTORS
+                        Path to graph in vectors format (.txt)
+  -o OUTPUT, --output OUTPUT
+                        Output file path (default: result.txt)
+  -s START, --start START
+                        Start vertex (used in bfs, sssp; default: 0)
+  -a ALPHA, --alpha ALPHA
+                        Damping factor (used in pr; default: 0.85)
+  -e EPS, --eps EPS
+                        Convergence tolerance (used in pr; default: 1e-4)
 ```
-
-### Required Arguments
-
-| Argument | Description |
-|----------|-------------|
-| `--algo` | Algorithm to run: `bfs`, `sssp`, `pr`, `tc` |
-| `-m` or `-v` | Input graph file. Use `-m` for Matrix Market (`.mtx`) or `-v` for vectors format (`.txt`). |
-
-### Optional Arguments
-
-| Option | Default | Description |
-|--------|---------|-------------|
-| `-o` | `result.txt` | Output file path |
-| `-s` | `0` | Start vertex (for BFS and SSSP) |
-| `-a` | `0.85` | Damping factor (for PageRank) |
-| `-e` | `1e-4` | Convergence tolerance (for PageRank) |
-| `-h`, `--help` | – | Show help message and exit |
-
 ---
 
 ## Examples
 
 ```bash
-# BFS on a Matrix Market file
+# BFS
 python main_spla.py --algo bfs -m graph.mtx -s 0 -o bfs_result.txt
 
-# SSSP on a vectors file (weighted graph)
-python main_spla.py --algo sssp -v weighted.txt -s 0 -o sssp_result.txt
+# SSSP
+python main_spla.py --algo sssp -v graph.txt -s 0 -o sssp_result.txt
 
-# PageRank with custom parameters
+# PageRank
 python main_spla.py --algo pr -v graph.txt -a 0.85 -e 1e-6 -o pr_result.txt
 
 # Triangle Counting
@@ -82,3 +90,34 @@ python main_spla.py --algo tc -m graph.mtx -o tc_result.txt
 python main_classic.py --algo bfs -v graph.txt -s 0 -o bfs_classic.txt
 ```
 
+---
+
+## Input format
+
+Two options:
+
+- `-v graph.txt` — Vectors format
+- `-m graph.mtx` — Matrix Market format (from https://sparse.tamu.edu/)
+
+
+### graph.txt
+
+Example 
+```text
+3          # number of vertices n 
+0 1 2      # I: source vertex indices
+1 2 0      # J: target vertex indices
+5 3 2      # V: edge weights 
+```
+
+### graph.mtx (Matrix Market)
+Example 
+```text
+%%MatrixMarket matrix coordinate real general
+% rows cols nnz
+4 4 4      #rows, cols, non-zero values
+1 2 1.0    #source vertex, target vertex, weight
+2 3 2.0
+3 4 3.0
+4 1 4.0
+```
\ No newline at end of file