deleted other unnecessary files

.gitignore

@@ -4,3 +4,5 @@ leo_sync.sh
 bb
 **.ipynb*
 scalability_results
+check.py
+var.py

check.py

-#!/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")
-

var.py

-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))