From bb07cd3067231716ea8262cbef098d81a1a06117 Mon Sep 17 00:00:00 2001
From: lykos98 <francy273998@gmail.com>
Date: Thu, 31 Oct 2024 16:15:24 +0100
Subject: [PATCH] deleted other unnecessary files
---
.gitignore | 2 +
check.py | 110 -----------------------------------------------------
var.py | 6 ---
3 files changed, 2 insertions(+), 116 deletions(-)
delete mode 100644 check.py
delete mode 100644 var.py
diff --git a/.gitignore b/.gitignore
index 7ea75a7..e583d9e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,3 +4,5 @@ leo_sync.sh
bb
**.ipynb*
scalability_results
+check.py
+var.py
diff --git a/check.py b/check.py
deleted file mode 100644
index 11072fc..0000000
--- a/check.py
+++ /dev/null
@@ -1,110 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.neighbors import NearestNeighbors
-
-ndims = 5
-k = 500
-p = 10
-
-with open("bb/top_nodes.csv","r") as f:
- l = f.readlines()
-
-def parse_lines(l,n_dims):
- ll = [line.split(",") for line in l]
- level = np.array([ int(line[0]) for line in ll])
- owner = np.array([ int(line[1]) for line in ll])
- split_dim = np.array([ int(line[2]) for line in ll])
- split_val = np.array([ float(line[3]) for line in ll])
- box_lb = np.array([ [float(el) for el in line[4:(4+n_dims)]] for line in ll])
- box_ub = np.array([ [float(el) for el in line[4 + n_dims:]] for line in ll])
- return level, owner, split_dim, split_val, box_lb, box_ub
-
-def plot_boxes(x,d0,d1,owner, split_dim, split_val, box_lb, box_ub, ratio = 0.7):
- from matplotlib.patches import Rectangle
- fig, ax = plt.subplots(figsize = (12 * ratio,10 * ratio))
- ax.scatter(x[:,d0],x[:,d1], s = 0.1)
- procs = np.where(owner != -1)
- for p in procs[0]:
- lbx = box_lb[p,d0]
- ubx = box_ub[p,d0]
- lby = box_lb[p,d1]
- uby = box_ub[p,d1]
- bw = ubx - lbx
- bh = uby - lby
- col = (np.random.rand(),np.random.rand(),np.random.rand(),0.5)
- ax.add_patch(Rectangle((lbx,lby),bw,bh, facecolor = col, label = owner[p]))
- plt.legend(loc = "lower left")
- #ax.add_patch(Rectangle((lbx,lby),2,2, facecolor = (np.random.rand(),np.random.rand(),np.random.rand(),0.3)))
-
-def plot_planes(x,d0,d1,owner, split_dim, split_val, box_lb, box_ub, ratio=0.7):
- from matplotlib.patches import Rectangle
- fig, ax = plt.subplots(figsize = (12 * ratio,10 * ratio))
- ax.scatter(x[:,d0],x[:,d1], s = 0.1)
- procs = np.where(owner == -1)[0]
- for p in procs:
- if split_dim[p] == d0:
- line_bounds = [box_lb[p,d1],box_ub[p,d1]]
- line_coord = split_val[p]
- #print("vline",split_dim[p],split_dim[p], line_bounds, line_coord)
- plt.vlines(line_coord, line_bounds[0], line_bounds[1], color = "y")
- elif split_dim[p] == d1:
- line_bounds = [box_lb[p,d0],box_ub[p,d0]]
- line_coord = split_val[p]
- #print("hline",split_dim[p],split_dim[p], line_bounds, line_coord)
- plt.hlines(line_coord, line_bounds[0], box_ub[p,d0], color = "y")
- plt.show()
-
-
-if __name__ == "__main__":
- level, owner, split_dim, split_val, box_lb, box_ub = parse_lines(l,ndims)
-
- #x = np.fromfile("../../robavaria/50_blobs_more_var.npy", np.float32)
- print("Loading data file")
- x = np.fromfile("./bb/ordered_data.npy", np.float64)
- x = x.reshape((x.shape[0]//ndims,ndims))
-
- #plot_boxes(x,0,1,owner,split_dim,split_val,box_lb,box_ub)
- #plot_planes(x,0,1,owner,split_dim,split_val,box_lb,box_ub)
-
- print("Loading ngbh results")
- ngbh = []
- for pp in range(p):
- ngbh.append(np.fromfile(f"./bb/rank_{pp}.ngbh", dtype = [("value","f8"),("array_idx","u8")]))
- ngbh = np.concatenate(ngbh)
-
- print("Searching for neighbors")
- nn = NearestNeighbors(n_jobs=-1,n_neighbors=k)
-
- nn.fit(x)
- dist, idx = nn.kneighbors(x)
-
- idx_c = ngbh["array_idx"]
- idx_c.shape
- dist_c = ngbh["value"]
-
-
- idx_c = idx_c.reshape((len(idx_c)//k,k))
- dist_c = dist_c.reshape((len(dist_c)//k,k))
-
- same_dist = 0
- sd_el = []
- abs_errors = 0
-
- print("Check")
- for i in range(len(idx_c)):
- r1 = idx[i]
- r2 = idx_c[i]
- w = np.where(r1 != r2)
- if len(w[0]) > 0:
- d1 = dist[i,w[0][0]]
- d2 = dist[i,w[0][1]]
- #print(i, w[0])
- if not np.isclose(d1,d2):
- abs_errors += 1
- same_dist += 1
- #print(" Found error in ", w[0], d1, d2)
- print(f"Found {abs_errors} errors")
-
diff --git a/var.py b/var.py
deleted file mode 100644
index 708cc54..0000000
--- a/var.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import numpy as np
-
-d = np.fromfile("../norm_data/std_LR_091_0000", dtype=np.float32)
-print(d.shape)
-d = d.reshape((d.shape[0]//5,5))
-print(np.cov(d.T))
--
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