From dcea267cf9649387cbfd01ef4257dac88b17a717 Mon Sep 17 00:00:00 2001 From: Warisa Date: Fri, 20 Sep 2024 20:02:20 +0700 Subject: [PATCH] test for vline, hline, and circle and all the results of plots --- .../benchmark_results_params_1_noise.png | Bin 0 -> 82472 bytes .../benchmark_results_params_2_noise.png | Bin 0 -> 83902 bytes .../benchmark_results_params_3_noise.png | Bin 0 -> 82627 bytes .../benchmark_results_params_4_noise.png | Bin 0 -> 86179 bytes .../benchmark_results_params_5_noise.png | Bin 0 -> 80982 bytes .../benchmark_results_params_6_noise.png | Bin 0 -> 85732 bytes .../benchmark_results_params_7_noise.png | Bin 0 -> 86809 bytes .../hline/hline_params_1_noise.png | Bin 0 -> 77445 bytes .../hline/hline_params_2_noise.png | Bin 0 -> 74525 bytes .../hline/hline_params_3_noise.png | Bin 0 -> 74364 bytes .../hline/hline_params_4_noise.png | Bin 0 -> 73686 bytes .../hline/hline_params_5_noise.png | Bin 0 -> 81641 bytes .../hline/hline_params_6_noise.png | Bin 0 -> 76191 bytes .../benchmark_results_params_1_noise.png | Bin 0 -> 73184 bytes .../benchmark_results_params_2_noise.png | Bin 0 -> 71895 bytes .../benchmark_results_params_3_noise.png | Bin 0 -> 77294 bytes .../benchmark_results_params_4_noise.png | Bin 0 -> 66585 bytes .../benchmark_results_params_5_noise.png | Bin 0 -> 66121 bytes .../benchmark_results_params_6_noise.png | Bin 0 -> 72155 bytes sandbox/parameter_init_circle.jl | 120 ++++++++++++++++++ sandbox/parameter_init_hline.jl | 102 +++++++++++++++ sandbox/parameter_init_vline.jl | 100 +++++++++++++++ 22 files changed, 322 insertions(+) create mode 100644 sandbox/param_performance_results/circle/benchmark_results_params_1_noise.png create mode 100644 sandbox/param_performance_results/circle/benchmark_results_params_2_noise.png create mode 100644 sandbox/param_performance_results/circle/benchmark_results_params_3_noise.png create mode 100644 sandbox/param_performance_results/circle/benchmark_results_params_4_noise.png create mode 100644 sandbox/param_performance_results/circle/benchmark_results_params_5_noise.png create mode 100644 sandbox/param_performance_results/circle/benchmark_results_params_6_noise.png create mode 100644 sandbox/param_performance_results/circle/benchmark_results_params_7_noise.png create mode 100644 sandbox/param_performance_results/hline/hline_params_1_noise.png create mode 100644 sandbox/param_performance_results/hline/hline_params_2_noise.png create mode 100644 sandbox/param_performance_results/hline/hline_params_3_noise.png create mode 100644 sandbox/param_performance_results/hline/hline_params_4_noise.png create mode 100644 sandbox/param_performance_results/hline/hline_params_5_noise.png create mode 100644 sandbox/param_performance_results/hline/hline_params_6_noise.png create mode 100644 sandbox/param_performance_results/vline/benchmark_results_params_1_noise.png create mode 100644 sandbox/param_performance_results/vline/benchmark_results_params_2_noise.png create mode 100644 sandbox/param_performance_results/vline/benchmark_results_params_3_noise.png create mode 100644 sandbox/param_performance_results/vline/benchmark_results_params_4_noise.png create mode 100644 sandbox/param_performance_results/vline/benchmark_results_params_5_noise.png create mode 100644 sandbox/param_performance_results/vline/benchmark_results_params_6_noise.png create mode 100644 sandbox/parameter_init_circle.jl create mode 100644 sandbox/parameter_init_hline.jl create mode 100644 sandbox/parameter_init_vline.jl diff --git a/sandbox/param_performance_results/circle/benchmark_results_params_1_noise.png b/sandbox/param_performance_results/circle/benchmark_results_params_1_noise.png new file mode 100644 index 0000000000000000000000000000000000000000..12e271e45ca218b2c90f188f830e61f8de01c94f GIT binary patch literal 82472 zcmeFZ2T)Y~_ASU~K%%06Bozsg1tf@og0>{dB3XjuBp^tRDnStu5CK6@qGXUH8AJ?7 zkc@&P14;$~5s)yezyC{9_2$*oyjL}^YNqeK%54wb=lsrZ@4fa~Yo9Ppbwz3lW(pD# 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LsqFit +using Plots # Import the Plots library +using BenchmarkTools # Import the BenchmarkTools library +using Interpolations +using LinearAlgebra + +# circle model function +function circle_model(xy, p) + x0, y0, r = p + x = xy[:, 1] + y = xy[:, 2] + return (x .- x0).^2 + (y .- y0).^2 .- r^2 +end + +function fit_circle_with_random_guess(xy) + # Random initial guess for circle fitting + p0 = rand(3) # Random initial guesses for a, b, c + println("Random initial guess: ", p0) + + # Perform the fit + fit = curve_fit(circle_model, xy, zeros(size(xy, 1)), p0) + + # Calculate error (sum of squared residuals) + fit_error = sum(fit.resid .^ 2) + + return p0, fit, fit_error +end + +# Improved function to fit a circle with a better initial guess +function fit_circle_with_improved_guess(xy) + + # Improved initial guess for circle fitting + x = xy[:, 1] + y = xy[:, 2] + + p0 = [mean(x), mean(y), sqrt(mean((x .- mean(x)).^2 .+ (y .- mean(y)).^2))] + + # Perform the fit + fit = curve_fit(circle_model, xy, zeros(size(xy, 1)), p0) + + # Calculate error (sum of squared residuals) + fit_error = sum(fit.resid .^ 2) + + return p0, fit, fit_error +end + +# Function to generate noisy circular data +function generate_noisy_circular_data(p, noise_level) + x0, y0, r = p + num_points = 100 + + θ = LinRange(0, 2π, num_points) + x = x0 .+ r .* cos.(θ) .+ noise_level .* randn(num_points) + y = y0 .+ r .* sin.(θ) .+ noise_level .* randn(num_points) + # Add noise + y_noisy = y .+ randn(length(y)) * noise_level # Adjust noise level as needed + + # Combine into xy matrix + xy = hcat(x, y_noisy) + return xy +end + +# Define the parameter combinations to test +# Define the parameter combinations to test +param_combinations = [ + [49, -49, 0.1], + [0.005, 0.005, 10], + [1, 1, 20], + [25, 25, 15], + [-25, -25, 30], + [50, 50, 5], + [-50, -50, 10] +] + +noise_levels = [0.0, 1.0, 5.0, 7.5, 10.0] + +for (i, params) in enumerate(param_combinations) + results = [] + for noise_level in noise_levels + xy = generate_noisy_circular_data(params, noise_level) + + # Benchmark improved initial guess + improved_benchmark = @benchmark fit_circle_with_improved_guess($xy) + improved_initial_guess, improved_fit, _ = fit_circle_with_improved_guess(xy) + improved_error = norm(improved_fit.param - params) + + # Benchmark random initial guess + random_benchmark = @benchmark fit_circle_with_random_guess($xy) + random_initial_guess, random_fit, _ = fit_circle_with_random_guess(xy) + random_error = norm(random_fit.param - params) + + push!(results, (noise_level, improved_benchmark, improved_error, random_benchmark, random_error)) + end + + # Extract time and memory allocation data + improved_times = [minimum(r[2]).time / 1e9 for r in results] # Convert to seconds + improved_memories = [minimum(r[2]).memory / 1024 for r in results] # Convert to KB + random_times = [minimum(r[4]).time / 1e9 for r in results] # Convert to seconds + random_memories = [minimum(r[4]).memory / 1024 for r in results] # Convert to KB + improved_errors = [r[3] for r in results] + random_errors = [r[5] for r in results] + + # Plot the results once for all noise levels + p = plot(layout = (3, 1), size = (800, 1200)) + + # Time plot + plot!(p[1], noise_levels, improved_times, label="Improved Time (s)", xlabel="Noise Level", ylabel="Time (s)", legend=:topright, title="Time Comparison (Params: $params)") + plot!(p[1], noise_levels, random_times, label="Random Time (s)", xlabel="Noise Level", ylabel="Time (s)", legend=:topright) + + # Memory plot + plot!(p[2], noise_levels, improved_memories, label="Improved Memory (KB)", xlabel="Noise Level", ylabel="Memory (KB)", legend=:topright, title="Memory Comparison (Params: $params)") + plot!(p[2], noise_levels, random_memories, label="Random Memory (KB)", xlabel="Noise Level", ylabel="Memory (KB)", legend=:topright) + + # Error plot + plot!(p[3], noise_levels, improved_errors, label="Improved Error", xlabel="Noise Level", ylabel="Error", legend=:topright, title="Error Comparison (Params: $params)") + plot!(p[3], noise_levels, random_errors, label="Random Error", xlabel="Noise Level", ylabel="Error", legend=:topright) + + # Save the plot to a file + savefig(p, "sandbox/param_performance_results/circle/benchmark_results_params_$(i)_noise.png") +end \ No newline at end of file diff --git a/sandbox/parameter_init_hline.jl b/sandbox/parameter_init_hline.jl new file mode 100644 index 0000000..f234b83 --- /dev/null +++ b/sandbox/parameter_init_hline.jl @@ -0,0 +1,102 @@ +using LsqFit +using Plots # Import the Plots library +using BenchmarkTools # Import the BenchmarkTools library +using Interpolations +using LinearAlgebra + +function hline_model(xy, p) + b = p + y = xy[:, 2] + return y .- b +end +# Function to fit a hline with a random initial guess +function fit_hline_with_random_guess(xy) + p0 = rand(1) # Random initial guesses for m, b + println("Random initial guess: ", p0) + + fit = curve_fit(hline_model, xy, zeros(size(xy, 1)), p0) + fit_error = sum(fit.resid .^ 2) + + return p0, fit, fit_error +end + +# Improved function to fit a hline with a better initial guess +function fit_hline_with_improved_guess(xy) + x_range = LinRange(-50, 50, 100) + y_range = LinRange(-50, 50, 100) + x = xy[:, 1] + y = xy[:, 2] + + b_guess = mean(y) + + p0 = [b_guess] + + fit = curve_fit(hline_model, xy, zeros(size(xy, 1)), p0) + fit_error = sum(fit.resid .^ 2) + + return p0, fit, fit_error +end + +# Function to generate noisy hlinear data +function generate_noisy_hlinear_data(p, noise_level) + b = p[1] + x = range(-50, 50, length=100) + y = [b for _ in x] + + y_noisy = y .+ randn(length(y)) * noise_level + xy = hcat(x, y_noisy) + return xy +end + +# Define the parameter combinations to test +param_combinations = [ + [0.005], + [0.5], + [1], + [5], + [10], + [50], +] + +noise_levels = [0.0, 1.0, 5.0, 7.5, 10.0] + +# Define the output path +output_path = "sandbox/param_performance_results/hline" + +for (i, params) in enumerate(param_combinations) + results = [] + for noise_level in noise_levels + xy = generate_noisy_hlinear_data(params, noise_level) + + improved_benchmark = @benchmark fit_hline_with_improved_guess($xy) + improved_initial_guess, improved_fit, _ = fit_hline_with_improved_guess(xy) + improved_error = norm(improved_fit.param - params) + + random_benchmark = @benchmark fit_hline_with_random_guess($xy) + random_initial_guess, random_fit, _ = fit_hline_with_random_guess(xy) + random_error = norm(random_fit.param - params) + + push!(results, (noise_level, improved_benchmark, improved_error, random_benchmark, random_error)) + end + + improved_times = [minimum(r[2]).time / 1e9 for r in results] + improved_memories = [minimum(r[2]).memory / 1024 for r in results] + random_times = [minimum(r[4]).time / 1e9 for r in results] + random_memories = [minimum(r[4]).memory / 1024 for r in results] + improved_errors = [r[3] for r in results] + random_errors = [r[5] for r in results] + + p = plot(layout=(3, 1), size=(800, 1200)) + + plot!(p[1], noise_levels, improved_times, label="Improved Time (s)", xlabel="Noise Level", ylabel="Time (s)", legend=:topright, title="Time Comparison (Params: $params)") + plot!(p[1], noise_levels, random_times, label="Random Time (s)", xlabel="Noise Level", ylabel="Time (s)", legend=:topright) + + plot!(p[2], noise_levels, improved_memories, label="Improved Memory (KB)", xlabel="Noise Level", ylabel="Memory (KB)", legend=:topright, title="Memory Comparison (Params: $params)") + plot!(p[2], noise_levels, random_memories, label="Random Memory (KB)", xlabel="Noise Level", ylabel="Memory (KB)", legend=:topright) + + plot!(p[3], noise_levels, improved_errors, label="Improved Error", xlabel="Noise Level", ylabel="Error", legend=:topright, title="Error Comparison (Params: $params)") + plot!(p[3], noise_levels, random_errors, label="Random Error", xlabel="Noise Level", ylabel="Error", legend=:topright) + + # Save the plot to the specified path + savefig(p, "$(output_path)_params_$(i)_noise.png") +end \ No newline at end of file diff --git a/sandbox/parameter_init_vline.jl b/sandbox/parameter_init_vline.jl new file mode 100644 index 0000000..d6ede6f --- /dev/null +++ b/sandbox/parameter_init_vline.jl @@ -0,0 +1,100 @@ +using LsqFit +using Plots # Import the Plots library +using BenchmarkTools # Import the BenchmarkTools library +using Interpolations +using LinearAlgebra + +function vline_model(xy, p) + c = p + x = xy[:, 1] + return x .- c +end + +# Function to fit a vline with a random initial guess +function fit_vline_with_random_guess(xy) + p0 = rand(1) # Random initial guesses for m, b + println("Random initial guess: ", p0) + + fit = curve_fit(vline_model, xy, zeros(size(xy, 1)), p0) + fit_error = sum(fit.resid .^ 2) + + return p0, fit, fit_error +end + +# Improved function to fit a vline with a better initial guess +function fit_vline_with_improved_guess(xy) + x_range = LinRange(-50, 50, 100) + y_range = LinRange(-50, 50, 100) + x = xy[:, 1] + y = xy[:, 2] + + c_guess = mean(x) + + p0 = [c_guess] + + fit = curve_fit(vline_model, xy, zeros(size(xy, 1)), p0) + fit_error = sum(fit.resid .^ 2) + + return p0, fit, fit_error +end + +# Function to generate noisy vlinear data +function generate_noisy_vlinear_data(p, noise_level) + c = p[1] + y = range(-50, 50, length=100) + x = [c for _ in y] + + y_noisy = y .+ randn(length(y)) * noise_level + xy = hcat(x, y_noisy) + return xy +end + +# Define the parameter combinations to test +param_combinations = [ + [0.005], + [0.5], + [1], + [5], + [10], + [50], +] + +noise_levels = [0.0, 1.0, 5.0, 7.5, 10.0] + +for (i, params) in enumerate(param_combinations) + results = [] + for noise_level in noise_levels + xy = generate_noisy_vlinear_data(params, noise_level) + + improved_benchmark = @benchmark fit_vline_with_improved_guess($xy) + improved_initial_guess, improved_fit, _ = fit_vline_with_improved_guess(xy) + improved_error = norm(improved_fit.param - params) + + random_benchmark = @benchmark fit_vline_with_random_guess($xy) + random_initial_guess, random_fit, _ = fit_vline_with_random_guess(xy) + random_error = norm(random_fit.param - params) + + push!(results, (noise_level, improved_benchmark, improved_error, random_benchmark, random_error)) + end + + improved_times = [minimum(r[2]).time / 1e9 for r in results] + improved_memories = [minimum(r[2]).memory / 1024 for r in results] + random_times = [minimum(r[4]).time / 1e9 for r in results] + random_memories = [minimum(r[4]).memory / 1024 for r in results] + improved_errors = [r[3] for r in results] + random_errors = [r[5] for r in results] + + p = plot(layout=(3, 1), size=(800, 1200)) + + plot!(p[1], noise_levels, improved_times, label="Improved Time (s)", xlabel="Noise Level", ylabel="Time (s)", legend=:topright, title="Time Comparison (Params: $params)") + plot!(p[1], noise_levels, random_times, label="Random Time (s)", xlabel="Noise Level", ylabel="Time (s)", legend=:topright) + + plot!(p[2], noise_levels, improved_memories, label="Improved Memory (KB)", xlabel="Noise Level", ylabel="Memory (KB)", legend=:topright, title="Memory Comparison (Params: $params)") + plot!(p[2], noise_levels, random_memories, label="Random Memory (KB)", xlabel="Noise Level", ylabel="Memory (KB)", legend=:topright) + + plot!(p[3], noise_levels, improved_errors, label="Improved Error", xlabel="Noise Level", ylabel="Error", legend=:topright, title="Error Comparison (Params: $params)") + plot!(p[3], noise_levels, random_errors, label="Random Error", xlabel="Noise Level", ylabel="Error", legend=:topright) + + # Save the plot to the specified path + savefig(p, "sandbox/param_performance_results/vline/benchmark_results_params_$(i)_noise.png") +end \ No newline at end of file