Hot Swapping models

1 files changed, 0 insertions, 429 deletions

diff --git a/src/main.c b/src/main.c
deleted file mode 100644
index ab96823..0000000
--- a/src/main.c
+++ /dev/null
@@ -1,429 +0,0 @@
-#include "crossbar_generator.h"
-#include "read_crossbar.h"
-#include "spires_interface.h"
-
-#include <math.h>
-#include <plplot/plplot.h>
-#include <spires.h>
-#include <stdio.h>
-#include <stdlib.h>
-
-#define NUM_NEURONS 400
-#define NUM_INPUTS 4
-#define NUM_OUTPUTS 2
-#define NUM_CROSSBAR_COLUMNS (NUM_OUTPUTS * 2)
-#define NUM_TRAINING_STEPS 500
-#define NUM_STEPS 2000
-
-#define SPIKE_THRESHOLD 0.9
-#define SPIKE_AMPLITUDE 0.1
-
-#define PI 3.14159265358979323846
-
-static int plot_raster(const Reservoir_State_Matrix *matrix,
-		       size_t neurons_to_plot, double spike_threshold);
-
-static int plot_reservoir_predictions(const double *expected,
-				      const double *predicted,
-				      size_t num_samples, size_t num_outputs,
-				      size_t output_to_plot);
-
-int main(void)
-{
-	// discrete LIF parameters for spires
-	double lif_config[] = {
-	    0.0, // V_off
-	    1.0, // V_th
-	    0.2, // leak rate
-	    0.5, // bias
-	};
-
-	const spires_reservoir_config config = {
-	    .num_neurons = NUM_NEURONS,
-	    .num_inputs = NUM_INPUTS,
-	    .num_outputs = NUM_OUTPUTS,
-	    .spectral_radius = 0.95,
-	    .ei_ratio = 0.8,
-	    .input_strength = 0.1,
-	    .connectivity = 0.1,
-	    .dt = 1.0,
-	    .connectivity_type = SPIRES_CONN_RANDOM,
-	    .neuron_type = SPIRES_NEURON_LIF_DISCRETE,
-	    .neuron_params = lif_config};
-
-	spires_reservoir *reservoir = NULL;
-
-	spires_status status = spires_reservoir_create(&config, &reservoir);
-
-	if (status != SPIRES_OK) {
-		fprintf(stderr, "Failed to create reservoir");
-		return -1;
-	}
-
-	// create training inputs
-	double training_inputs[NUM_TRAINING_STEPS * NUM_INPUTS];
-	for (size_t timestep = 0; timestep < NUM_TRAINING_STEPS; timestep++) {
-		for (size_t input = 0; input < NUM_INPUTS; input++) {
-			double signal =
-			    0.7 * sin(2.0 * PI * (double)timestep / 50.0) +
-			    0.3 * sin(2.0 * PI * (double)timestep / 17.0);
-			training_inputs[timestep * NUM_INPUTS + input] = signal;
-		}
-	}
-
-	// create target outputs
-	double target_outputs[NUM_TRAINING_STEPS * NUM_OUTPUTS];
-	for (size_t timestep = 0; timestep < NUM_TRAINING_STEPS; timestep++) {
-		size_t next_timestep = (timestep + 1) % NUM_TRAINING_STEPS;
-
-		double target =
-		    0.7 * sin(2.0 * PI * (double)next_timestep / 50.0) +
-		    0.3 * sin(2.0 * PI * (double)next_timestep / 17.0);
-		for (size_t output = 0; output < NUM_OUTPUTS; output++) {
-			target_outputs[timestep * NUM_OUTPUTS + output] =
-			    target;
-		}
-	}
-
-	Reservoir_State_Matrix state_matrix = {0};
-	if (collect_reservoir_states(reservoir, training_inputs,
-				     NUM_TRAINING_STEPS, &state_matrix) != 0) {
-		fprintf(stderr, "Failed to collect reservoir states");
-		spires_reservoir_destroy(reservoir);
-		return -1;
-	}
-	printf("collected state matrix: %zu x %zu\n", state_matrix.num_samples,
-	       state_matrix.num_features);
-
-	// training the readout layer
-	const double lambda = 1.0e-4;
-	int training_status =
-	    train_reservoir(reservoir, training_inputs, target_outputs,
-			    NUM_TRAINING_STEPS, lambda);
-	if (training_status < 0) {
-		fprintf(stderr, "Failed to train the reservoir");
-		free_reservoir_state_matrix(&state_matrix);
-		spires_reservoir_destroy(reservoir);
-		return -1;
-	}
-
-	// generate raster plot for verification
-	if (plot_raster(&state_matrix, NUM_NEURONS, SPIKE_THRESHOLD) != 0) {
-		fprintf(stderr, "Failed to plot raster\n");
-	}
-
-	// copy readout weights and convert to conductances
-	double *initial_resistances = NULL;
-	conductance_mapping mapping;
-
-	if (convert_weights_to_resistances(
-		reservoir, NUM_NEURONS, NUM_OUTPUTS, 1000.0, 100000.0,
-		&initial_resistances, &mapping) != 0) {
-		return -1;
-	}
-
-	double *row_voltages =
-	    malloc(state_matrix.num_features * state_matrix.num_samples *
-		   sizeof(double));
-
-	if (row_voltages == NULL) {
-		fprintf(stderr, "Failed to allocate spikes voltages");
-		free(initial_resistances);
-		free_reservoir_state_matrix(&state_matrix);
-		spires_reservoir_destroy(reservoir);
-		return -1;
-	}
-
-	for (size_t sample = 0; sample < state_matrix.num_samples; sample++) {
-		for (size_t neuron = 0; neuron < state_matrix.num_features;
-		     neuron++) {
-			size_t index =
-			    sample * state_matrix.num_features + neuron;
-
-			row_voltages[index] =
-			    SPIKE_AMPLITUDE * state_matrix.states[index];
-		}
-	}
-
-	const Crossbar_Config crossbar_config = {
-	    .rows = state_matrix.num_features,
-	    .columns = NUM_CROSSBAR_COLUMNS,
-	    .input_series = row_voltages,
-	    .num_samples = state_matrix.num_samples,
-	    .initial_resistance = initial_resistances,
-	    .model_path = "models/hp_memristor.cir",
-	    .subcircuit_name = "memristor",
-	    .load_resistance = 50.0,
-	    .time_step = 1e-6,
-	    .stop_time = state_matrix.num_samples * 1e-6,
-	    .print_state_nodes = 0};
-
-	if (generate_crossbar("output/crossbar.cir", &crossbar_config) < 0) {
-		fprintf(stderr, "failed to create crossbar config");
-		free(initial_resistances);
-		free_reservoir_state_matrix(&state_matrix);
-		spires_reservoir_destroy(reservoir);
-		return -1;
-	}
-	printf("Generated crossbar!!");
-
-	// call ngspice for crossbar
-	if (run_ngspice("output/crossbar.cir") < 0) {
-		fprintf(stderr, "Failed to run_ngspice");
-		free(initial_resistances);
-		free_reservoir_state_matrix(&state_matrix);
-		spires_reservoir_destroy(reservoir);
-		return -1;
-	}
-	printf("ran ngspice!!");
-
-	// crossbar parameters needed for reading
-	Crossbar_Output_Matrix crossbar_output = {
-	    .num_samples = NUM_TRAINING_STEPS,
-	    .num_outputs = NUM_CROSSBAR_COLUMNS,
-	    .time = NULL,
-	    .voltages = NULL};
-
-	if (read_crossbar("output/crossbar_output.dat", NUM_CROSSBAR_COLUMNS,
-			  &crossbar_output) < 0) {
-		fprintf(stderr, "Failed to read crossbar output file");
-		free(initial_resistances);
-		free_reservoir_state_matrix(&state_matrix);
-		spires_reservoir_destroy(reservoir);
-		free_crossbar_output_matrix(&crossbar_output);
-		return -1;
-	}
-	printf("read the crossbar outputs!!\n");
-
-	double *decoded_outputs =
-	    malloc(NUM_OUTPUTS * NUM_TRAINING_STEPS * sizeof(double));
-	if (decoded_outputs == NULL) {
-		fprintf(stderr,
-			"Failed to allocate memory for decoded outputs");
-	}
-	if (convert_output_to_software(
-		NUM_NEURONS, NUM_OUTPUTS, NUM_TRAINING_STEPS,
-		crossbar_output.voltages, initial_resistances,
-		crossbar_config.load_resistance, &mapping, row_voltages,
-		SPIKE_AMPLITUDE, decoded_outputs) < 0) {
-		fprintf(stderr,
-			"Failed to convert crossbar outputs back to software");
-		return -1;
-	}
-
-	// comparing prediction
-	for (int i = 0; i < NUM_TRAINING_STEPS; i++) {
-		printf("expected: %g , predicted: %g\n", target_outputs[i],
-		       decoded_outputs[i]);
-	}
-
-	// plotting expected vs. prediction
-	plot_reservoir_predictions(target_outputs, decoded_outputs,
-				   NUM_TRAINING_STEPS, NUM_OUTPUTS, 0);
-
-	printf("YAY IT WORKED!!! Cleaning up :)");
-	free(initial_resistances);
-	// free(spikes_voltages);
-	free(row_voltages);
-	free(decoded_outputs);
-	free_reservoir_state_matrix(&state_matrix);
-	spires_reservoir_destroy(reservoir);
-	free_crossbar_output_matrix(&crossbar_output);
-
-	return 0;
-}
-
-static int plot_raster(const Reservoir_State_Matrix *matrix,
-		       size_t neurons_to_plot, double spike_threshold)
-{
-	if (!matrix || !matrix->states || matrix->num_samples == 0) {
-		return -1;
-	}
-
-	if (neurons_to_plot > matrix->num_features) {
-		neurons_to_plot = matrix->num_features;
-	}
-
-	// count spikes
-	size_t spike_count = 0;
-	for (size_t t = 0; t < matrix->num_samples; t++) {
-		for (size_t n = 0; n < neurons_to_plot; n++) {
-			double value =
-			    matrix->states[t * matrix->num_features + n];
-			if (value > spike_threshold) {
-				spike_count++;
-			}
-		}
-	}
-
-	if (spike_count == 0) {
-		fprintf(stderr, "No spikes found above threshold %.3f\n",
-			spike_threshold);
-		return -1;
-	}
-
-	PLFLT *x = malloc(spike_count * sizeof(*x));
-	PLFLT *y = malloc(spike_count * sizeof(*y));
-	if (!x || !y) {
-		free(x);
-		free(y);
-		return -1;
-	}
-
-	// fill spike coordinates
-	size_t k = 0;
-	for (size_t t = 0; t < matrix->num_samples; t++) {
-		for (size_t n = 0; n < neurons_to_plot; n++) {
-			double value =
-			    matrix->states[t * matrix->num_features + n];
-			if (value > spike_threshold) {
-				x[k] = (PLFLT)t;
-				y[k] = (PLFLT)n;
-				k++;
-			}
-		}
-	}
-
-	// output to png
-	plsdev("svg");
-	plsfnam("output/reservoir_raster.svg");
-
-	plsetopt("geometry", "1600x1200");
-	plscolbg(255, 255, 255);
-
-	plinit();
-
-	plscol0(1, 40, 40, 40); // gray axis
-	plscol0(2, 0, 0, 0);	// blue points
-
-	plcol0(1);
-	plwidth(1.0);
-
-	plenv(0.0, (PLFLT)(matrix->num_samples - 1), 0.0,
-	      (PLFLT)(neurons_to_plot - 1), 0, 0);
-
-	pllab("Timestep", "Neuron index", "SPIRES Reservoir Raster Plot");
-
-	plcol0(2);
-	plwidth(1.0);
-
-	for (size_t i = 0; i < spike_count; i++) {
-		PLFLT xline[2] = {x[i], x[i]};
-		PLFLT yline[2] = {y[i] - 0.35, y[i] + 0.35};
-
-		plline(2, xline, yline);
-	}
-
-	plend();
-
-	free(x);
-	free(y);
-	return 0;
-}
-
-static int plot_reservoir_predictions(const double *expected,
-				      const double *predicted,
-				      size_t num_samples, size_t num_outputs,
-				      size_t output_to_plot)
-{
-	PLFLT *x;
-	PLFLT *y_expected;
-	PLFLT *y_predicted;
-	PLFLT y_min;
-	PLFLT y_max;
-
-	if (!expected || !predicted || num_samples == 0 ||
-	    output_to_plot >= num_outputs)
-		return -1;
-
-	x = malloc(num_samples * sizeof(*x));
-	y_expected = malloc(num_samples * sizeof(*y_expected));
-	y_predicted = malloc(num_samples * sizeof(*y_predicted));
-
-	if (!x || !y_expected || !y_predicted) {
-		free(x);
-		free(y_expected);
-		free(y_predicted);
-		return -1;
-	}
-
-	y_min = (PLFLT)expected[output_to_plot];
-	y_max = y_min;
-
-	for (size_t sample = 0; sample < num_samples; sample++) {
-		size_t index;
-
-		index = sample * num_outputs + output_to_plot;
-
-		x[sample] = (PLFLT)sample;
-		y_expected[sample] = (PLFLT)expected[index];
-		y_predicted[sample] = (PLFLT)predicted[index];
-
-		if (y_expected[sample] < y_min)
-			y_min = y_expected[sample];
-
-		if (y_expected[sample] > y_max)
-			y_max = y_expected[sample];
-
-		if (y_predicted[sample] < y_min)
-			y_min = y_predicted[sample];
-
-		if (y_predicted[sample] > y_max)
-			y_max = y_predicted[sample];
-	}
-
-	{
-		PLFLT margin;
-
-		margin = (y_max - y_min) * 0.1;
-
-		if (margin == 0.0)
-			margin = 1.0;
-
-		y_min -= margin;
-		y_max += margin;
-	}
-
-	plsdev("svg");
-	plsfnam("output/reservoir_prediction.svg");
-	plsetopt("geometry", "1600x1000");
-
-	plscolbg(255, 255, 255);
-	plinit();
-
-	plscol0(1, 0, 0, 0);
-	plscol0(2, 30, 90, 200);
-	plscol0(3, 200, 50, 50);
-
-	plcol0(1);
-	plwidth(1.0);
-
-	plenv(0.0, (PLFLT)(num_samples - 1), y_min, y_max, 0, 0);
-
-	pllab("Timestep", "Output", "Expected vs SPICE Crossbar Prediction");
-
-	plcol0(2);
-	plwidth(2.0);
-	plline((PLINT)num_samples, x, y_expected);
-
-	plcol0(3);
-	plwidth(2.0);
-	plline((PLINT)num_samples, x, y_predicted);
-
-	plcol0(1);
-	plcol0(2);
-	plptex((PLFLT)(num_samples * 0.75), y_max - 0.08 * (y_max - y_min), 1.0,
-	       0.0, 0.0, "Expected");
-
-	plcol0(3);
-	plptex((PLFLT)(num_samples * 0.75), y_max - 0.16 * (y_max - y_min), 1.0,
-	       0.0, 0.0, "Predicted");
-
-	plend();
-
-	free(x);
-	free(y_expected);
-	free(y_predicted);
-
-	return 0;
-}