Placeholder_text correction and adding hints

Bayes Guards SMS/Predict/task.md

@@ -33,32 +33,86 @@ delete the `pass` operator.
   help of the `split_by_words()` function.
 - In each message, find a set of unique words and create a vector of zeros of
   the same size.
+
+<div class="hint">
+Create a zero-initialized array to store the index of each unique word.
+
+```python
+index_array = np.zeros(unique.shape, dtype=np.int64)
+```
+</div>
+
 - For each unique word from the list, find a correspondence in the dictionary; if you find it,
   write its index to the vector created in the previous step, if not – write an index
   equal to the dictionary length. All such words have the same
   probability.
+
+<div class="hint">
+
+For each word, assign its dictionary index to `word_index`; if the word is not found, set `word_index` to `dict_size`.
+
+```python
+    word_index = self.dictionary[word] if word in self.dictionary else self.dict_size
+```
+
+**Why do this?**
+
+Because `self.likelihood` stores word probabilities by index rather than the string itself.
+
+</div>
+
 - Calculate `log_likelihood` by applying the `np.log()` function to the slice of the `likelihood` array
   obtained with the help of `index_array`; thus, the array will contain the probabilities of only
   those words that occur in our sentence. 
-- Use the above formula to calculate the most probable class for this message.
-- Return the list of most probable classes of all messages in the input array.
 
+<div class="hint">
 
-Then, implement the `score` method, which passes the testing sample through the algorithm, compares the received
-class labels with the real ons and returns the proportion of correctly classified objects.
+`self.likelihood[c, w]` represents the probability of word `w` for class `c`.
+Select the probabilities for the message words and calculate their logs:
 
-<div class="hint">
-Posterior probabilities for each class are calculated as the sum of the prior probability logarithm and the summarized logarithms of probabilities for the words from 
-<code>log_likelihood</code> 
-positioned along one axis, i.e., separately for each class.
+```python
+log_likelihood = np.log(self.likelihood[:, index_array])
+```
 </div>
 
-<div class="hint">
+- Compute the posterior score for each class.
+
+<div class="hint" title="Posterior meaning">
+The posterior score indicates the probability of each class given the message.
+Compute it by summing the log-probabilities of the words (across <code>axis=1</code>) and adding the log prior.
+</div>
+
+<div class="hint" title="Posterior formula">
+
+```python
+posterior = np.log(self.classes_prior) + np.sum(log_likelihood, axis=1)
+```
+</div>
+
+- Identify the class with the highest score for each message.
+
+<div class="hint" title="Find the best class">
 After finding the posterior probabilities for classes, you need to determine which 
 one is the largest among them and choose a class corresponding to it from <code>unique_classes</code>. Here, 
 you may use the <a href="https://numpy.org/doc/stable/reference/generated/numpy.argmax.html">numpy.argmax</a> function.
 </div>
 
+<div class="hint" title="Prediction formula">
+
+```python
+predicted = self.unique_classes[np.argmax(posterior)]
+```
+</div>
+
+Then, implement the `score` method, which passes the test samples through the algorithm, compares the predicted
+class labels with the true labels, and returns the proportion of correctly classified objects.
+
+<div class="hint">
+Use <code>predict</code> to generate labels, compare them with the true labels <code>y</code>, and return the fraction of matches:
+
+<pre><code>return np.sum(self.predict(X) == y) / len(y)</code></pre>
+</div>
+
 To see the results of your code, you can add the following
 lines to the `main` block in `task.py` and then run it:
 

Comic-Con and K-means/Reading an image/task-info.yaml

@@ -3,12 +3,12 @@ files:
   - name: task.py
     visible: true
     placeholders:
-      - offset: 156
+      - offset: 153
         length: 16
         placeholder_text: "# TODO"
-      - offset: 243
+      - offset: 231
         length: 30
-        placeholder_text: "# Reshape the image"
+        placeholder_text: "# TODO"
   - name: tests/test_task.py
     visible: false
     propagatable: false

Comic-Con and K-means/Reading an image/task.py

@@ -3,10 +3,9 @@
 
 
 def read_image(path='superman-batman.png'):
-    # Here, we load the image using PIL's open function.
+    # Here, load the image using PIL's open function.
     image = Image.open(path)
-    # We reshape the image into an (M x N, 3)
-    # array.
+    # Reshape the image into an (M x N, 3) array.
     return np.array(image).reshape(-1, 3)
 
 

Horror Trees/Conclusion/task.md

@@ -40,5 +40,5 @@ To see the results of the algorithm's work, run the code in the `halloween.py` f
 see an image `tree.jpg` in the list of files in Course View – it is our decision tree!
 
 #### Additional information links: 
-1) An [example](https://iq.opengenus.org/id3-algorithm/#:~:text=ID3%20algorithm%2C%20stands%20for%20Iterative,or%20minimum%20Entropy%20(H)) of using the ID3 algorithm in weather forecasts.
+1) An [example](https://discourse.opengenus.org/t/using-id3-algorithm-to-build-a-decision-tree-to-predict-the-weather/3343) of using the ID3 algorithm in weather forecasts.
 2) An [article](https://en.wikipedia.org/wiki/ID3_algorithm#cite_ref-2) in Wikipedia on ID3. 

Horror Trees/Information Gain/task.md

@@ -15,18 +15,29 @@ We subtract entropy `Y` for the condition `X` from entropy `Y` to calculate the
 `Y`, provided that there is some additional knowledge `X` about `Y`.
 
 
-
 ### Task
 
 Implement the `information_gain` method, which 
 takes a sample, divides it into two independent sub-samples, and calculates the information gain.
 To divide the sample, use the `divide` method written
-in the previous step. 
+in the previous step.
+
+<div class="hint" title="Division into two subsets">
 
+Use the divide `method` from the `Predicate` class to split the sample into two subsets:
 
+`X1, y1, X2, y2 = self.divide(X, y)`
+
+</div>
 
+<div class="hint" title="p value">
+Each subset contributes to the total entropy proportionally to its size. Compute the fraction of samples in the first subset as follows:
+
+`p = float(len(X1)) / len(X)`
+
+</div>
 
-<div class="hint">
+<div class="hint" title="Information gain formula">
 
 To calculate information gain, you can use the above formula in the following way:
 

Horror Trees/Node/node.py

@@ -4,6 +4,8 @@
 # the true and false branches.
 class Node:
     def __init__(self, column=-1, value=None, true_branch=None, false_branch=None):
+        # Implement the four attributes of the Node class:
+        # column, value, true_branch, false_branch
         self.column = column
         self.value = value
         self.true_branch = true_branch

Horror Trees/Node/task-info.yaml

@@ -3,9 +3,9 @@ files:
   - name: node.py
     visible: true
     placeholders:
-      - offset: 333
+      - offset: 443
         length: 127
-        placeholder_text: "# TODO: Implement the four attributes of the Node class"
+        placeholder_text: "# TODO"
   - name: task.py
     visible: true
   - name: tests/test_task.py

Horror Trees/Predict/task.md

@@ -13,10 +13,73 @@ In `classify_subtree`, you need to:
 
 1. Check whether `sub_tree` is an instance of the `Node` class and if yes, return the
    current value of `sub_tree`, as in such a case, it is a class label.
+
+<div class="hint">
+
+If `sub_tree` is not a `Node`, return it; it already represents the class label.
+
+```python
+if not isinstance(sub_tree, Node):
+    return sub_tree
+```
+</div>
+
 2. Compare the characteristic value from the column according to which a condition is set in the given node with the threshold value.
+
+<div class="hint">
+
+Each node evaluates a specific feature of the object being classified. The index of the feature to check is stored in `sub_tree.column`.
+You need to extract the corresponding value from `x`.
+
+```python
+v = x[sub_tree.column]
+```
+
+</div>
+
 3. Depending on the result, choose the tree branch along which you will proceed (`true_branch` or `false_branch`).
+
+<div class="hint" title="Compare numeric features">
+
+For numeric features, the node evaluates a threshold (e.g., `age >= 30`). 
+Compare the feature value against this threshold to determine which branch to follow.
+
+```python
+if isinstance(v, int) or isinstance(v, float):
+    if v >= sub_tree.value:
+        branch = sub_tree.true_branch
+    else:
+        branch = sub_tree.false_branch
+```
+
+</div>
+
+<div class="hint" title="Compare categorical features">
+
+For categorical features, the node evaluates an equality condition (e.g., `color == "red"`).
+Determine the next branch based on whether the feature value matches this criterion.
+
+```python
+else:
+    if v == sub_tree.value:
+        branch = sub_tree.true_branch
+    else:
+        branch = sub_tree.false_branch
+```
+
+</div>
+
 4. Repeat these actions recursively until the result will be a class label (a leaf node).
 
+<div class="hint">
+
+Choosing a branch is only the first step – you may encounter another node.
+Apply the same logic again by calling the function on the selected branch until you reach a leaf (the final class label).
+
+```python
+return self.classify_subtree(x, branch)
+```
+</div>
 
 To see the results of your code, add the following lines
 to the `main` block in `task.py` and run it:

Iris Network/Backpropagation/task.md

@@ -70,9 +70,42 @@ In the `network.py` file, implement only the `backward` method of the `NN` class
 
 <ul>
 <li>Calculate the error for the output layer (<code>delta_l2</code>) as the difference between the network results (<code>output</code>) and the real class labels (<code>y</code>) multiplied elementwise by the derivative of the activation function for output ($\delta_{o}$ formula).</li>
-<li>Calculate the error for the hidden layer (<code>delta_l1</code>) as the product of input layer error matrices and the weights <code>w2</code> multiplied elementwise by the derivative of the activation function wrt the output data of the hidden layer (<code>layer1</code>) ($\delta_{h}$ formula).</li>
+
+<div class="hint" title="Result">
+
+```python
+delta_l2 = (y - output) * sigmoid_derivative(output)
+```
+</div>
+
+<li>Calculate the error for the hidden layer (<code>delta_l1</code>) by taking the product of the output layer error 
+and the transpose of the weight matrix <code>w2</code>, then multiplying element-wise by the derivative 
+of the activation function with respect to the hidden layer's output (<code>layer1</code>) ($\delta_{h}$ formula).</li>
+
+<div class="hint" title="Result">
+
+```python
+delta_l1 = np.dot(delta_l2, self.w2.T) * sigmoid_derivative(self.layer1)
+```
+</div>
+
 <li>Adjust the weight coefficients of the output layer (<code>w2</code>) by calculating the vector product of the hidden layer (<code>layer1</code>) and the output layer error (<code>delta_l2</code>) multiplied elementwise by the learning rate (formula 3).</li>
+
+<div class="hint" title="Result">
+
+```python
+self.w2 += (np.dot(self.layer1.T, delta_l2) * learning_rate)
+```
+</div>
+
 <li>Adjust the weight coefficients of the hidden layer (<code>w1</code>) by calculating the vector product of the input layer (<code>X</code>) and the hidden layer error (<code>delta_l1</code>), multiplied elementwise by the learning rate (formula 3).</li>
+
+<div class="hint" title="Result">
+
+```python
+self.w1 += (np.dot(X.T, delta_l1) * learning_rate)
+```
+</div>
 </ul>
 
 Before you start, delete the `pass` operator and uncomment all lines that are not task commentaries.

Iris Network/Train and Predict/task.md

@@ -5,13 +5,29 @@ The process of setting up a neural network involves successive implementation of
 In the `network.py` file, implement the `train` method of the `NN` class. Besides data, it takes the `n_iter` parameter, which sets
 the necessary number of iterations. The method should call two other (previously implemented) methods in the right order. It does not return anything.
 
+<div class="hint">
+On each iteration, generate predictions via <code>feedforward</code> and update the model's parameters using <code>backward</code> propagation.
+
+```python
+        for itr in range(n_iter):
+            l2 = self.feedforward(X)
+            self.backward(X, y, l2)
+```
+</div>
+
 Augment the implementation by the `predict` method, which passes all objects from the `X` matrix through the trained neural network.
 
-Before you start, delete the `pass` operator and uncomment all lines that are not task commentaries.
+<div class="hint"> 
+The <code>predict</code> method is a required part of the neural network's interface.
+We will implement it here, even though it simply acts as a wrapper for the <code>feedforward</code> method. 
+
+```python
+return self.feedforward(X)
+```
+While this case is straightforward, other scenarios may require a more complex implementation.
+</div>
 
-<div class="hint"> The <code>predict</code> method is a part of the interface of a program the neural network is expected to include, so we will implement it 
-despite the fact that it just calls the <code>feedforward</code> method. It's a lucky coincidence – in other cases, there might be
-something else.</div>
+Before you begin, delete the `pass` statement and uncomment all lines that are not task-related comments.
 
 To see the results of your code in this step, add the following lines to the `main` block in `task.py`:
 

Pima Indians Diabetes and Linear Classifier/Gradient Descent/task-info.yaml

@@ -21,7 +21,7 @@ files:
         placeholder_text: "# TODO: Set it to the new ones"
       - offset: 2210
         length: 28
-        placeholder_text: "# Return the predicted classes"
+        placeholder_text: "# TODO"
   - name: loss_functions.py
     visible: true
   - name: task.py

Pima Indians Diabetes and Linear Classifier/Read data/task-info.yaml

@@ -3,18 +3,18 @@ files:
   - name: task.py
     visible: true
     placeholders:
-      - offset: 314
+      - offset: 256
         length: 35
         placeholder_text: "# TODO"
-      - offset: 802
+      - offset: 532
         length: 36
-        placeholder_text: "# Standardize the dataset"
-      - offset: 1000
+        placeholder_text: "# TODO"
+      - offset: 703
         length: 60
-        placeholder_text: "# Add a column of -1 to the left of X"
-      - offset: 1145
+        placeholder_text: "# TODO"
+      - offset: 812
         length: 12
-        placeholder_text: "# {0, 1} -> {1, -1}"
+        placeholder_text: "# TODO"
   - name: tests/test_task.py
     visible: false
     propagatable: false