From e75b13af12bd6279650ec18bcf154f0ac1431a40 Mon Sep 17 00:00:00 2001
From: MRIDUL JAIN <105979087+Spinachboul@users.noreply.github.com>
Date: Wed, 10 Jan 2024 17:40:13 +0530
Subject: [PATCH] Update tensor_prod.md

---
 docs/src/tensor_prod.md | 116 +++++++++++++++++++++++++++++++---------
 1 file changed, 91 insertions(+), 25 deletions(-)

diff --git a/docs/src/tensor_prod.md b/docs/src/tensor_prod.md
index c20902c1..664a5c69 100644
--- a/docs/src/tensor_prod.md
+++ b/docs/src/tensor_prod.md
@@ -2,39 +2,105 @@
 The tensor product function is defined as:
 ``\[ f(x) = ∏ᵢ=₁ᵈ cos(aπxᵢ) \]``
 
-Let's import Surrogates and Plots:
-```@example tensor
-using Surrogates
-using Plots
-default()
+Where\
+d: Respresents the dimensionality of the input vector x\
+xi: Represents the ith components of the input vector\
+a: A constant parameter
+
+# Generating Data and Plotting
+
 ```
+function tensor_product_function(x, a)
+    return prod(cos.(a * π * xi) for xi in x)
+end
 
-Define the 1D objective function:
-```@example tensor
-function f(x)
-    a = 0.5;
-    return cos(a*pi*x)
+# Generate training and test data
+function generate_data(n, lb, ub, a)
+    x_train = sample(n, lb, ub, SobolSample())
+    y_train = tensor_product_function(x_train, a)
+    
+    x_test = sample(1000, lb, ub, SobolSample())  # Generating test data
+    y_test = tensor_product_function(x_test, a)  # Generating test labels
+    
+    return x_train, y_train, x_test, y_test
 end
-```
 
-```@example tensor
+# Visualize training data and the true function
+function plot_data_and_true_function(x_train, y_train, x_test, y_test, a, lb, ub)
+    xs = range(lb, ub, length=1000)
+    
+    scatter(x_train, y_train, label="Training points", xlims=(lb, ub), ylims=(-1, 1), legend=:top)
+    plot!(xs, tensor_product_function.(Ref(xs), a), label="True function", legend=:top)
+    scatter!(x_test, y_test, label="Test points")
+end
+
+# Generate data and plot
 n = 30
 lb = -5.0
 ub = 5.0
 a = 0.5
-x = sample(n, lb, ub, SobolSample())
-y = f.(x)
-xs = lb:0.001:ub
-scatter(x, y, label="Sampled points", xlims=(lb, ub), ylims=(-1, 1), legend=:top)
-plot!(xs, f.(xs), label="True function", legend=:top)
+
+x_train, y_train, x_test, y_test = generate_data(n, lb, ub, a)
+plot_data_and_true_function(x_train, y_train, x_test, y_test, a, lb, ub)
+```
+
+# Training various Surrogates
+Now let's train various surrogate models and evaluate their performance on the test data
+
+```
+# Train different surrogate models
+function train_surrogates(x_train, y_train)
+    loba = LobachevskySurrogate(x_train, y_train)
+    krig = Kriging(x_train, y_train)
+    return loba, krig
+end
+
+# Evaluate and compare surrogate model performances
+function evaluate_surrogates(loba, krig, x_test)
+    loba_pred = loba(x_test)
+    krig_pred = krig(x_test)
+    return loba_pred, krig_pred
+end
+
+# Plot surrogate predictions against the true function
+function plot_surrogate_predictions(loba_pred, krig_pred, y_test, a, lb, ub)
+    xs = range(lb, ub, length=1000)
+    
+    plot(xs, tensor_product_function.(Ref(xs), a), label="True function", legend=:top)
+    plot!(xs, loba_pred, label="Lobachevsky")
+    plot!(xs, krig_pred, label="Kriging")
+end
+
+# Train surrogates and evaluate their performance
+loba, krig = train_surrogates(x_train, y_train)
+loba_pred, krig_pred = evaluate_surrogates(loba, krig, x_test)
+
+# Plot surrogate predictions against the true function
+plot_surrogate_predictions(loba_pred, krig_pred, y_test, a, lb, ub)
 ```
 
-Fitting and plotting different surrogates:
-```@example tensor
-loba_1 = LobachevskySurrogate(x, y, lb, ub)
-krig = Kriging(x, y, lb, ub)
-scatter(x, y, label="Sampled points", xlims=(lb, ub), ylims=(-2.5, 2.5), legend=:bottom)
-plot!(xs,f.(xs), label="True function", legend=:top)
-plot!(xs, loba_1.(xs), label="Lobachevsky", legend=:top)
-plot!(xs, krig.(xs), label="Kriging", legend=:top)
+# Reporting the best Surrogate Model
+To determine the best surrogate, you can compare their accuracy and performance metrics on the test data. For instance, you can calculate and compare the mean squared error (MSE) or any other relevant metric
+
 ```
+using Statistics
+
+# Evaluate performance metrics
+function calculate_performance_metrics(pred, true_vals)
+    return mean((pred .- true_vals).^2)
+end
+
+# Compare surrogate model performances
+mse_loba = calculate_performance_metrics(loba_pred, y_test)
+mse_krig = calculate_performance_metrics(krig_pred, y_test)
+
+if mse_loba < mse_krig
+    println("Lobachevsky Surrogate is the best with MSE: ", mse_loba)
+else
+    println("Kriging Surrogate is the best with MSE: ", mse_krig)
+end
+```
+
+This structure provides a framework for generating data, training various 
+surrogate models, evaluating their performance on test data, and reporting 
+the best surrogate based on performance metrics like MSE. Adjustments can made to suit the specific evaluation criteria or additional surrogate models.