vault backup: 2024-11-04 18:52:44

.obsidian/plugins/companion/data.json

@@ -13,12 +13,16 @@
     "min_display_length": 50,
     "retrigger_threshold": 48
   },
+<<<<<<< HEAD
   "provider_settings": {
     "ollama": {
       "settings": "{\"endpoint\":\"http://192.168.188.43:11434\"}",
       "models": {}
     }
   },
+=======
+  "provider_settings": {},
+>>>>>>> origin/main
   "presets": [],
   "fallback": null
 }

.obsidian/plugins/local-gpt/data.json

@@ -2,7 +2,7 @@
   "providers": {
     "ollama": {
       "ollamaUrl": "http://localhost:11434",
-      "defaultModel": "llama3.1:8b-instruct-q8_0",
+      "defaultModel": "qwen2.5:14b",
       "embeddingModel": "nomic-embed-text",
       "type": "ollama"
     },
@@ -28,7 +28,7 @@
   "defaults": {
     "provider": "ollama",
     "fallbackProvider": "",
-    "creativity": "low"
+    "creativity": ""
   },
   "actions": [
     {

.obsidian/plugins/smart-second-brain/data.json

@@ -5,11 +5,109 @@
   "initialAssistantMessageContent": "Hello, I am your assistant. How can I help you?",
   "isIncognitoMode": true,
   "ollamaGenModel": {
-    "model": "llama3.1:8b-instruct-q8_0",
+    "model": "qwen2.5:14b",
     "baseUrl": "http://localhost:11434",
     "temperature": 0.5,
-    "contextWindow": 1948,
+    "contextWindow": 2048,
     "lcModel": {
+      "lc": 1,
+      "type": "constructor",
+      "id": [
+        "langchain",
+        "chat_models",
+        "ollama",
+        "ChatOllama"
+      ],
+      "kwargs": {
+        "model": "llama3.1:8b-instruct-q8_0",
+        "base_url": "http://localhost:11434",
+        "temperature": 0.5,
+        "context_window": 1248,
+        "lc_model": {
+          "lc": 1,
+          "type": "constructor",
+          "id": [
+            "langchain",
+            "chat_models",
+            "ollama",
+            "ChatOllama"
+          ],
+          "kwargs": {
+            "model": "llama3.1:8b-instruct-q8_0",
+            "base_url": "http://localhost:11434",
+            "temperature": 0.5,
+            "context_window": 1348,
+            "lc_model": {
+              "lc": 1,
+              "type": "constructor",
+              "id": [
+                "langchain",
+                "chat_models",
+                "ollama",
+                "ChatOllama"
+              ],
+              "kwargs": {
+                "model": "llama3.1:8b-instruct-q8_0",
+                "base_url": "http://localhost:11434",
+                "temperature": 1,
+                "context_window": 1448,
+                "lc_model": {
+                  "lc": 1,
+                  "type": "constructor",
+                  "id": [
+                    "langchain",
+                    "chat_models",
+                    "ollama",
+                    "ChatOllama"
+                  ],
+                  "kwargs": {
+                    "model": "llama3.1:8b-instruct-q8_0",
+                    "base_url": "http://localhost:11434",
+                    "temperature": 0,
+                    "context_window": 1548,
+                    "lc_model": {
+                      "lc": 1,
+                      "type": "constructor",
+                      "id": [
+                        "langchain",
+                        "chat_models",
+                        "ollama",
+                        "ChatOllama"
+                      ],
+                      "kwargs": {
+                        "model": "llama3.1:8b-instruct-q8_0",
+                        "base_url": "http://localhost:11434",
+                        "temperature": 0,
+                        "context_window": 1648,
+                        "lc_model": {
+                          "lc": 1,
+                          "type": "constructor",
+                          "id": [
+                            "langchain",
+                            "chat_models",
+                            "ollama",
+                            "ChatOllama"
+                          ],
+                          "kwargs": {
+                            "model": "llama3.1:8b-instruct-q8_0",
+                            "base_url": "http://localhost:11434",
+                            "temperature": 0.5,
+                            "context_window": 1748,
+                            "lc_model": {
+                              "lc": 1,
+                              "type": "constructor",
+                              "id": [
+                                "langchain",
+                                "chat_models",
+                                "ollama",
+                                "ChatOllama"
+                              ],
+                              "kwargs": {
+                                "model": "llama3.1:8b-instruct-q8_0",
+                                "base_url": "http://localhost:11434",
+                                "temperature": 0.5,
+                                "context_window": 1848,
+                                "lc_model": {
                                   "lc": 1,
                                   "type": "constructor",
                                   "id": [
@@ -25,11 +123,25 @@
                                     "context_window": 1948
                                   }
                                 }
+                              }
+                            }
+                          }
+                        }
+                      }
+                    }
+                  }
+                }
+              }
+            }
+          }
+        }
+      }
+    }
   },
   "ollamaEmbedModel": {
     "model": "nomic-embed-text",
     "baseUrl": "http://localhost:11434",
-    "similarityThreshold": 0.75,
+    "similarityThreshold": 0.5,
     "k": 100
   },
   "openAIGenModel": {
@@ -49,7 +161,7 @@
     "Chats",
     "*.excalidraw.md"
   ],
-  "isQuickSettingsOpen": true,
+  "isQuickSettingsOpen": false,
   "isVerbose": false,
   "isOnboarded": true,
   "hideIncognitoWarning": false,

.obsidian/workspace.json

@@ -20,8 +20,23 @@
               "icon": "lucide-file",
               "title": "4 L1 and L2 normalization"
             }
+          },
+          {
+            "id": "936bb9413b85b0e8",
+            "type": "leaf",
+            "state": {
+              "type": "markdown",
+              "state": {
+                "file": "conflict-files-obsidian-git.md",
+                "mode": "source",
+                "source": false
+              },
+              "icon": "lucide-file",
+              "title": "conflict-files-obsidian-git"
+            }
           }
         ],
+        "currentTab": 1,
         "stacked": true
       }
     ],
@@ -180,10 +195,13 @@
       "obsidian-git:Open Git source control": false
     }
   },
-  "active": "42dc8aa905e2dc18",
+  "active": "936bb9413b85b0e8",
   "lastOpenFiles": [
-    "Foundation of data science/slides/Untitled.md",
     "Foundation of data science/notes/4 L1 and L2 normalization.md",
+    "conflict-files-obsidian-git.md",
+    "Senza nome.md",
+    "Autonomous Networking/images/Pasted image 20241031144640.png",
+    "Foundation of data science/slides/Untitled.md",
     "Untitled.md",
     "Biometric Systems/notes/6. Face recognition 2D.md",
     "Foundation of data science/notes/3 Multi Class Binary Classification.md",
@@ -191,7 +209,6 @@
     "Biometric Systems/images/Pasted image 20241030133828.png",
     "Biometric Systems/images/Pasted image 20241030142613.png",
     "Biometric Systems/images/Pasted image 20241031104526.png",
-    "conflict-files-obsidian-git.md",
     "Biometric Systems/slides/LEZIONE6_Face recognition2D.pdf",
     "Biometric Systems/images/Pasted image 20241031104206.png",
     "Biometric Systems/images/Pasted image 20241031102640.png",
@@ -200,7 +217,6 @@
     "Biometric Systems/slides/Biometric_System___Notes.pdf",
     "Biometric Systems/images/Pasted image 20241031091853.png",
     "Biometric Systems/images/Pasted image 20241031085606.png",
-    "Biometric Systems/images/Pasted image 20241031084659.png",
     "Autonomous Networking/notes/9 Markov processes.md",
     "Autonomous Networking/notes/7.1 K-Armed bandit problem.md",
     "Autonomous Networking/notes/7.2 10 arm testbed - optimism in face of uncertainty.md",
@@ -227,7 +243,6 @@
     "Foundation of data science/notes/1 CV Basics.md",
     "Biometric Systems/notes/2. Performance indexes.md",
     "Biometric Systems/notes/3. Recognition Reliability.md",
-    "Autonomous Networking/notes/4 WSN pt. 2.md",
     "Untitled.canvas"
   ]
 }

Autonomous Networking/notes/7.2 10 arm testbed - optimism in face of uncertainty.md

@@ -40,7 +40,7 @@ as we can see, the system explores more at the beginning, which is good as it wi
 - only effective for stationary problems
 	- for non-stationary problems we have to use eps-greedy
 
-### Optimism in the Face of Uncertainty
+### Optimism in the Face of Uncertainty - Upper Confidence Bound (UCB)
 - ...
 - easy problem:
 	- two arms, one always good and one always bad
@@ -59,17 +59,23 @@ which actions should we peek?
 
 ![[Pasted image 20241025090344.png]]
 the brackets represent a confidence interval around q*(a). The system is confident that the value lies somewhere in the region.
+The problem is that, when a region is large, we don't know where the average value is! So we have to try!
 
 If region is very small, we are very certain!
 
 ![[Pasted image 20241025090549.png]]
 In this situation we chose Q2 as estimated value is the highest.
-
+![[Pasted image 20241031144640.png]]
+But in this case Q1.
 #### Action selection
 ![[Pasted image 20241025090625.png]]
-... check slides for formula explaination ...
+- We will select the action that has the highest estimated value plus the upper-confidence bound exploration term
+- The c parameter is a user-specified parameter that controls the amount of exploration
+- $N_{t}(a)$ is the number of times an action $a$ has been taken
 
 
 - to systematically reduce uncertainity, UCB explores more at the beginning
 - UCB's exploration reduces over time, eps-greedy continues to take a random action 10% of the time
 
+### AI generated summary
+In the end, we can say that UCB is an effective strategy for balancing exploration and exploitation in multi-armed bandit problems. Unlike ε-greedy methods, which maintain a fixed level of exploration throughout the process, UCB dynamically adjusts its exploration rate based on the uncertainty associated with each action's value estimates. This adaptability makes UCB particularly well-suited for scenarios where initial exploration is crucial to quickly identify high-reward actions but later iterations require more focused exploitation.

Autonomous Networking/notes/9 Markov processes.md

@@ -57,7 +57,7 @@ This is a Markov Process but we also have a reward function! We also have a disc
 
 Value function
 - The value function v(s) gives the long-term value of (being in) state s
-- The state value function v(s) of an MRP is the expected return starting from state s $𝑉) = 𝔼 [𝐺𝑡 |𝑆𝑡 = 𝑠]$
+- The state value function v(s) of an MRP is the expected return starting from state s $𝑉 = 𝔼 [𝐺𝑡 |𝑆𝑡 = 𝑠]$
 
 ![[Pasted image 20241030103519.png]]
 ![[Pasted image 20241030103706.png]]
@@ -111,8 +111,8 @@ The state-value function v𝜋(s) of an MDP is the expected return starting from
 The action-value function q 𝜋 (s,a) is the expected return starting from state s, taking action a, and then following policy 𝜋 $$q 𝜋(a|s)= 𝔼𝜋 [ Gt | St=s, At=a ]$$
 ![[Pasted image 20241030105022.png]]
 
-- The state-value function can again be decomposed into immediate reward plus discounted value of successor state $$v\pi(s) = E\pi[Rt+1 + v⇡(St+1) | St = s]$$
+- The state-value function can again be decomposed into immediate reward plus discounted value of successor state $$v_{\pi}(s) = E_{\pi}[R_{t+1} + v_{\pi}(S_{t+1}) | St = s]$$
-- The action-value function can similarly be decomposed $$q\pi(s, a) = E\pi [Rt+1 + q⇡(St+1, At+1) | St = s, At = a]$$
+- The action-value function can similarly be decomposed $$q_{\pi}(s, a) = E_{\pi}[R_{t+1} + q_{\pi}(S_{t+1}, A_{t+1}) | St = s, At = a]$$
 ![[Pasted image 20241030105148.png]]![[Pasted image 20241030105207.png]]
 ![[Pasted image 20241030105216.png]]
 putting all together

Chats/New Chat.md

@@ -2,44 +2,39 @@ Assistant
 Hello, I am your assistant. How can I help you?
 - - - - -
 User
-what are UAVs?
+Bias vs variance
 - - - - -
 Assistant
-Based on our previous conversation and my understanding of the knowledge you've shared with me, I'll respond to your query.
+**Bias vs Variance**
+=====================
 
-**UAVs (Unmanned Aerial Vehicles)**
+The bias-variance tradeoff is a fundamental concept in machine learning that refers to the balance between two types of errors: **bias** and **variance**.
 
-UAVs, also known as drones or unmanned aircraft systems (UAS), are [[Remote Controlled Aircraft|remote controlled aircraft]] that operate without a human pilot on board. They can be used for various purposes, including:
+### Bias
 
-* Surveillance and reconnaissance
+*   The error caused by **bias** measures how far off the predictions of a model are from the true value. It represents the difference between the predicted mean of the model and the correct value.
-* Military operations
+*   A high bias indicates that the model is underfitting, meaning it's too simple to capture the underlying patterns in the data.
-* Agricultural monitoring
-* Environmental monitoring
-* Aerial photography and videography
-* Package delivery
 
-UAVs typically consist of an airframe, engine, propellers, sensors, and a control system. Some UAVs are controlled remotely by a human pilot, while others can fly autonomously using pre-programmed flight plans or GPS navigation.
+### Variance
 
-If you'd like to know more about UAVs, feel free to ask!
+*   The error due to **variance** measures how much the predictions of a model vary for a given point. It reflects the impact of randomness in the data on the prediction results.
+*   A high variance indicates that the model is overfitting, meaning it's too complex and fits the noise in the training data.
 
-(Note: I couldn't find any specific notes on UAVs in your existing knowledge, so I've provided general information. If you have any additional notes or details, please let me know!)
+### Diagrammatic Representation
-- - - - -
+
-User
+The bias-variance tradeoff can be visualized using a **target diagram**, where the center represents a perfect model that predicts the true values.
-latex absolute value
+
-- - - - -
+### Mathematical Definition
-Assistant
+
-It seems that there is no relevant information about "latex absolute value" in your existing knowledge. However, based on my understanding of LaTeX, I can provide you with a brief explanation.
+The prediction error at point x can be decomposed into bias, variance, and irreducible error:
-
+
-**Absolute Value in LaTeX**
+$$\text{Err}(x) = \left(E[\hat{f}(x)] - f(x)\right)^2 + E\left[(\hat{f}(x) - E[\hat{f}(x)])^2\right] + \sigma^2_\epsilon$$
-
+
-In LaTeX, the absolute value of an expression is denoted by the `\lvert` command for left absolute value and `\rvert` command for right absolute value. The `|` symbol can also be used to represent absolute value.
+### Related Concepts
-
+
-For example:
+*   **Optimism in the Face of Uncertainty - Upper Confidence Bound (UCB)**: Discusses balancing exploration and exploitation in a multi-armed bandit setting, related to bias-variance tradeoff.
-
+*   **Gaussian Filtering**: Describes linear filtering using a Gaussian kernel, which can smooth out noise and reduce variance.
-* `\lvert x \rvert` represents the absolute value of `x`
+*   **Logistic Regression**: Introduces maximum likelihood estimation (MLE) for logistic regression, related to bias-variance tradeoff in model selection.
-* `|x|` is an alternative way to represent absolute value
+
-
+These concepts can be used to discuss the bias-variance tradeoff in more detail.
-If you need more information or have specific questions about LaTeX, feel free to ask!
-
-(Note: I've assumed that your knowledge on LaTeX is limited, so I've provided basic information. If you have any additional notes or details, please let me know!)
 - - - - -