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#-------------------------------------------------------------

#
# Implements the k-Means clustering algorithm
#
# INPUT PARAMETERS:
# ----------------------------------------------------------------------------
# NAME  TYPE   DEFAULT  MEANING
# ----------------------------------------------------------------------------
# X     String   ---    Location to read matrix X with the input data records
# k     Int      ---    Number of centroids
# runs  Int       10    Number of runs (with different initial centroids)
# maxi  Int     1000    Maximum number of iterations per run
# tol   Double 0.000001 Tolerance (epsilon) for WCSS change ratio
# samp  Int       50    Average number of records per centroid in data samples
# C     String  "C.mtx" Location to store the output matrix with the centroids
# isY   Boolean FALSE   do not write Y
# Y     String  "Y.mtx" Location to store the mapping of records to centroids
# fmt   String  "text"  Matrix output format, usually "text" or "csv"
# verb  Boolean FALSE   do not print per-iteration stats
# ----------------------------------------------------------------------------
#
# Example:
# hadoop jar SystemDS.jar -f Kmeans.dml -nvargs X=X.mtx k=5 C=centroids.mtx
# hadoop jar SystemDS.jar -f Kmeans.dml -nvargs X=X.mtx k=5 runs=100 maxi=5000 tol=0.00000001 samp=20 C=centroids.mtx isY=TRUE Y=clusters.mtx verb=TRUE

fileX = $X;
fileY = ifdef ($Y, "Y.mtx");
fileC = ifdef ($C, "C.mtx");

num_centroids = $k;
num_runs   = ifdef ($runs, 10);      # $runs=10;
max_iter   = ifdef ($maxi, 1000);    # $maxi=1000;
eps        = ifdef ($tol, 0.000001); # $tol=0.000001;
is_write_Y = ifdef ($isY, FALSE);    # $isY=FALSE;
is_verbose = ifdef ($verb, FALSE);   # $verb=FALSE;
fmtCY      = ifdef ($fmt, "text");   # $fmt="text";
avg_sample_size_per_centroid = ifdef ($samp, 50);  # $samp=50;


print ("BEGIN K-MEANS SCRIPT");
print ("Reading X...");

# X : matrix of data points as rows
X = read (fileX);
num_records   = nrow (X);
num_features  = ncol (X);

sumXsq = sum (X ^ 2);
# Remark - A useful rewrite: sum (A %*% B) = sum (t(colSums(A)) * rowSums(B))

# STEP 1: INITIALIZE CENTROIDS FOR ALL RUNS FROM DATA SAMPLES:

print ("Taking data samples for initialization...");

[sample_maps, samples_vs_runs_map, sample_block_size] = 
    get_sample_maps (num_records, num_runs, num_centroids * avg_sample_size_per_centroid);

is_row_in_samples = rowSums (sample_maps);
X_samples = sample_maps %*% X;
X_samples_sq_norms = rowSums (X_samples ^ 2);

print ("Initializing the centroids for all runs...");
All_Centroids = matrix (0, rows = (num_runs * num_centroids), cols = num_features);

# We select centroids according to the k-Means++ heuristic applied to a sample of X
# Loop invariant: min_distances ~ sq.distances from X_sample rows to nearest centroids,
# with the out-of-range X_sample positions in min_distances set to 0.0

min_distances = is_row_in_samples;  # Pick the 1-st centroids uniformly at random

for (i in 1 : num_centroids)
{
    # "Matricize" and prefix-sum to compute the cumulative distribution function:
    min_distances_matrix_form = 
        matrix (min_distances, rows = sample_block_size, cols = num_runs, byrow = FALSE);
    cdf_min_distances = cumsum (min_distances_matrix_form);
    
    # Select the i-th centroid in each sample as a random sample row id with
    # probability ~ min_distances:
    random_row = Rand (rows = 1, cols = num_runs, min = 0.0, max = 1.0);  
    threshold_matrix = random_row * cdf_min_distances [sample_block_size, ];
    centroid_ids = t(colSums (cdf_min_distances < threshold_matrix)) + 1;
    
    # Place the selected centroids together, one per run, into a matrix:
    centroid_placer = table (seq (1, num_runs), 
        sample_block_size * seq (0, num_runs - 1) + centroid_ids, num_runs, sample_block_size * num_runs);
    centroids = centroid_placer %*% X_samples;
    
    # Place the selected centroids into their appropriate slots in All_Centroids:
    centroid_placer = table (seq (i, num_centroids * (num_runs - 1) + i, num_centroids), 
        seq (1, num_runs, 1), nrow (All_Centroids), num_runs);
    All_Centroids = All_Centroids + centroid_placer %*% centroids;
    
    # Update min_distances to preserve the loop invariant:
    distances = X_samples_sq_norms + samples_vs_runs_map %*% rowSums (centroids ^ 2)
              - 2 * rowSums (X_samples * (samples_vs_runs_map %*% centroids));
    min_distances = ifelse(i==1, is_row_in_samples*distances, min(min_distances,distances));
}

# STEP 2: PERFORM K-MEANS ITERATIONS FOR ALL RUNS:

termination_code = matrix (0, rows = num_runs, cols = 1);
final_wcss = matrix (0, rows = num_runs, cols = 1);
num_iterations = matrix (0, rows = num_runs, cols = 1);

print ("Performing k-means iterations for all runs...");

parfor (run_index in 1 : num_runs, check = 0)
{
    C = All_Centroids [(num_centroids * (run_index - 1) + 1) : (num_centroids * run_index), ];
    C_old = C;
    iter_count = 0;
    term_code = 0;
    wcss = Inf

    while (term_code == 0)
    {
        # Compute Euclidean squared distances from records (X rows) to centroids (C rows)
        # without the C-independent term, then take the minimum for each record
        D = -2 * (X %*% t(C)) + t(rowSums (C ^ 2));
        minD = rowMins (D);
        # Compute the current centroid-based within-cluster sum of squares (WCSS)
        wcss_old = wcss;
        wcss = sumXsq + sum (minD);
        if (is_verbose == TRUE) {
            if (iter_count == 0)
                print ("Run " + run_index + ", At Start-Up:  Centroid WCSS = " + wcss);
            else
                print ("Run " + run_index + ", Iteration " + iter_count + ":  Centroid WCSS = " + wcss
                    + ";  Centroid change (avg.sq.dist.) = " + (sum ((C - C_old) ^ 2) / num_centroids));
        }
        
        # Find the closest centroid for each record
        P = D <= minD;
        # If some records belong to multiple centroids, share them equally
        P = P / rowSums (P);
        # Compute the column normalization factor for P
        P_denom = colSums (P);
        # Compute new centroids as weighted averages over the records
        C_new = (t(P) %*% X) / t(P_denom);
        
        # Check if convergence or maximum iteration has been reached
        iter_count = iter_count + 1
        if(wcss_old - wcss < eps * wcss)
           term_code = 1; # Convergence reached
        else if(iter_count >= max_iter) 
           term_code = 2; # Max iteration reached
        else if(sum (P_denom <= 0) > 0)
           term_code = 3; # "Runaway" centroid (0.0 denom)
        else {
           C_old = C; C = C_new; 
        }
    }
    print ("Run " + run_index + ", Iteration " + iter_count + ":  Terminated with code = " + term_code + ",  Centroid WCSS = " + wcss);
    All_Centroids [(num_centroids * (run_index - 1) + 1) : (num_centroids * run_index), ] = C;
    final_wcss [run_index, 1] = wcss;
    termination_code [run_index, 1] = term_code;
    num_iterations [run_index, 1] = iter_count;
}

# STEP 3: SELECT THE RUN WITH BEST CENTROID-WCSS AND OUTPUT ITS CENTROIDS:

termination_bitmap = matrix (0, rows = num_runs, cols = 3);
termination_bitmap_raw = table (seq (1, num_runs, 1), termination_code);
termination_bitmap [, 1 : ncol(termination_bitmap_raw)] = termination_bitmap_raw;
termination_stats = colSums (termination_bitmap);
print ("Number of successful runs = " + as.integer (as.scalar (termination_stats [1, 1])));
print ("Number of incomplete runs = " + as.integer (as.scalar (termination_stats [1, 2])));
print ("Number of failed runs (with lost centroids) = " + as.integer (as.scalar (termination_stats [1, 3])));

num_successful_runs = as.integer(as.scalar (termination_stats [1, 1]));
if (num_successful_runs > 0) {
    final_wcss_successful = replace(target = final_wcss, pattern = Inf, replacement = 0)* termination_bitmap [, 1];
    worst_wcss = max (final_wcss_successful);
    best_wcss = min (final_wcss_successful + (10 * worst_wcss + 10) * (1 - termination_bitmap [, 1]));
    avg_wcss = sum (final_wcss_successful) / num_successful_runs;
    best_index_vector = (final_wcss_successful == best_wcss);
    aggr_best_index_vector = cumsum (best_index_vector);
    best_index = as.integer (sum (aggr_best_index_vector == 0) + 1);
    print ("Successful runs:  Best run is " + best_index + " with Centroid WCSS = " + best_wcss 
        + ";  Avg WCSS = " + avg_wcss + ";  Worst WCSS = " + worst_wcss);
    C = All_Centroids [(num_centroids * (best_index - 1) + 1) : (num_centroids * best_index), ];
    print ("Writing out the best-WCSS centroids...");
    write (C, fileC, format=fmtCY);
    if (is_write_Y == TRUE) {
        print ("Writing out the best-WCSS cluster labels...");
        D =  -2 * (X %*% t(C)) + t(rowSums (C ^ 2));
        P = (D <= rowMins (D));
        aggr_P = t(cumsum (t(P)));
        Y = rowSums (aggr_P == 0) + 1
        write (Y, fileY, format=fmtCY);
    }
    print ("DONE.");
} else {
    stop ("No output is produced.  Try increasing the number of iterations and/or runs.");
}


get_sample_maps = function (int num_records, int num_samples, int approx_sample_size)
    return (Matrix[double] sample_maps, Matrix[double] sample_col_map, int sample_block_size)
{
    if (approx_sample_size < num_records) {
        # Input value "approx_sample_size" is the average sample size; increase it by ~10 std.dev's
        # to get the sample block size (to allocate space):
        sample_block_size = as.integer (approx_sample_size + round (10 * sqrt (approx_sample_size)));
        num_rows = sample_block_size * num_samples;
        
        # Generate all samples in parallel by converting uniform random values into random
        # integer skip-ahead intervals and prefix-summing them:
        sample_rec_ids = Rand (rows = sample_block_size, cols = num_samples, min = 0.0, max = 1.0);
        sample_rec_ids = round (log (sample_rec_ids) / log (1.0 - approx_sample_size / num_records) + 0.5);
        # Prob [k-1 < log(uniform)/log(1-p) < k] = p*(1-p)^(k-1) = Prob [k-1 zeros before a one]
        sample_rec_ids = cumsum (sample_rec_ids);  #  (skip to next one) --> (skip to i-th one)
        
        # Replace all sample record ids over "num_records" (i.e. out of range) by "num_records + 1":
        is_sample_rec_id_within_range = (sample_rec_ids <= num_records);
        sample_rec_ids = sample_rec_ids * is_sample_rec_id_within_range 
                       + (num_records + 1) * (1 - is_sample_rec_id_within_range);
        
        # Rearrange all samples (and their out-of-range indicators) into one column-vector:
        sample_rec_ids = 
            matrix (sample_rec_ids, rows = num_rows, cols = 1, byrow = FALSE);
        is_row_in_samples = 
            matrix (is_sample_rec_id_within_range, rows = num_rows, cols = 1, byrow = FALSE);

        # Use contingency table to create the "sample_maps" matrix that is a vertical concatenation
        # of 0-1-matrices, one per sample, each with 1s at (i, sample_record[i]) and 0s elsewhere:
        sample_maps = table (seq (1, num_rows), sample_rec_ids, num_rows, num_records);
        
        # Create a 0-1-matrix that maps each sample column ID into all row positions of the
        # corresponding sample; map out-of-sample-range positions to row id = num_rows + 1:
        sample_positions = (num_rows + 1) - is_row_in_samples * seq (num_rows, 1, -1);
        # Column ID positions = 1, 1, ..., 1, 2, 2, ..., 2, . . . , n_c, n_c, ..., n_c:
        col_positions = round (0.5 + seq (0, num_rows - 1, 1) / sample_block_size);
        sample_col_map = table (sample_positions, col_positions);
        # Remove the out-of-sample-range positions by cutting off the last row:
        sample_col_map = sample_col_map [1 : (num_rows), ];
        
    } else {
        one_per_record = matrix (1, rows = num_records, cols = 1);
        sample_block_size = num_records;
        sample_maps    = matrix (0, rows = (num_records * num_samples), cols = num_records);
        sample_col_map = matrix (0, rows = (num_records * num_samples), cols = num_samples);
        for (i in 1:num_samples) {
            sample_maps    [(num_records * (i - 1) + 1) : (num_records * i),  ] = diag (one_per_record);
            sample_col_map [(num_records * (i - 1) + 1) : (num_records * i), i] = one_per_record;
}   }   }