new updates
This commit is contained in:
Morten Hjorth-Jensen
@@ -0,0 +1,201 @@
|
||||
GroundTruth,DT_Pred,RF_Pred,GB_Pred
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
0,0,0,0
|
||||
1,1,1,1
|
||||
|
@@ -0,0 +1,483 @@
|
||||
import numpy as np
|
||||
|
||||
class DecisionTreeClassifier:
|
||||
def __init__(self, criterion='gini', max_depth=None, min_samples_split=2, max_features=None):
|
||||
self.criterion = criterion
|
||||
self.max_depth = max_depth
|
||||
self.min_samples_split = min_samples_split
|
||||
self.max_features = max_features
|
||||
self.tree = None
|
||||
|
||||
class Node:
|
||||
def __init__(self, feature=None, threshold=None, left=None, right=None, *, value=None):
|
||||
self.feature = feature
|
||||
self.threshold = threshold
|
||||
self.left = left
|
||||
self.right = right
|
||||
self.value = value # Leaf class label
|
||||
|
||||
def fit(self, X, y):
|
||||
X, y = np.array(X), np.array(y)
|
||||
self.n_features_ = X.shape[1]
|
||||
self.tree = self._build_tree(X, y, depth=0)
|
||||
return self
|
||||
|
||||
def _build_tree(self, X, y, depth):
|
||||
num_samples, _ = X.shape
|
||||
# Stop if conditions met
|
||||
if num_samples < self.min_samples_split or (self.max_depth is not None and depth >= self.max_depth) or len(np.unique(y)) == 1:
|
||||
leaf_val = self._majority_class(y)
|
||||
return DecisionTreeClassifier.Node(value=leaf_val)
|
||||
# Find best split
|
||||
feat_idx, thr = self._best_split(X, y)
|
||||
if feat_idx is None:
|
||||
leaf_val = self._majority_class(y)
|
||||
return DecisionTreeClassifier.Node(value=leaf_val)
|
||||
# Split data
|
||||
left_mask = X[:, feat_idx] <= thr
|
||||
left_node = self._build_tree(X[left_mask], y[left_mask], depth+1)
|
||||
right_node = self._build_tree(X[~left_mask], y[~left_mask], depth+1)
|
||||
return DecisionTreeClassifier.Node(feature=feat_idx, threshold=thr, left=left_node, right=right_node)
|
||||
|
||||
def _best_split(self, X, y):
|
||||
best_gain = 0
|
||||
best_feat, best_thr = None, None
|
||||
if self.criterion == 'gini':
|
||||
base_impurity = self._gini(y)
|
||||
else:
|
||||
base_impurity = self._entropy(y)
|
||||
n_features = X.shape[1]
|
||||
features = range(n_features)
|
||||
# Possibly sample subset of features (for Random Forest use)
|
||||
if self.max_features is not None:
|
||||
if isinstance(self.max_features, int) and self.max_features < n_features:
|
||||
features = np.random.choice(n_features, self.max_features, replace=False)
|
||||
elif isinstance(self.max_features, float):
|
||||
k = int(n_features * self.max_features)
|
||||
features = np.random.choice(n_features, k, replace=False)
|
||||
for feat in features:
|
||||
X_col = X[:, feat]
|
||||
unique_vals = np.unique(X_col)
|
||||
if len(unique_vals) <= 1:
|
||||
continue
|
||||
# Try midpoints between sorted unique values
|
||||
thresholds = (unique_vals[:-1] + unique_vals[1:]) / 2.0
|
||||
for thr in thresholds:
|
||||
left_mask = X_col <= thr
|
||||
y_left, y_right = y[left_mask], y[~left_mask]
|
||||
if len(y_left) == 0 or len(y_right) == 0:
|
||||
continue
|
||||
# Compute impurity of the split
|
||||
if self.criterion == 'gini':
|
||||
imp_left = self._gini(y_left)
|
||||
imp_right = self._gini(y_right)
|
||||
else:
|
||||
imp_left = self._entropy(y_left)
|
||||
imp_right = self._entropy(y_right)
|
||||
p = float(len(y_left)) / len(y)
|
||||
gain = base_impurity - (p * imp_left + (1 - p) * imp_right)
|
||||
if gain > best_gain:
|
||||
best_gain, best_feat, best_thr = gain, feat, thr
|
||||
return best_feat, best_thr
|
||||
|
||||
def _gini(self, y):
|
||||
_, counts = np.unique(y, return_counts=True)
|
||||
p = counts / counts.sum()
|
||||
return 1.0 - np.sum(p**2)
|
||||
|
||||
def _entropy(self, y):
|
||||
_, counts = np.unique(y, return_counts=True)
|
||||
p = counts / counts.sum()
|
||||
p = p[p > 0]
|
||||
return -np.sum(p * np.log2(p))
|
||||
|
||||
def _majority_class(self, y):
|
||||
unique, counts = np.unique(y, return_counts=True)
|
||||
return unique[np.argmax(counts)]
|
||||
|
||||
def predict(self, X):
|
||||
X = np.array(X)
|
||||
return np.array([self._predict_input(x, self.tree) for x in X])
|
||||
|
||||
def _predict_input(self, x, node):
|
||||
if node.value is not None:
|
||||
return node.value
|
||||
if x[node.feature] <= node.threshold:
|
||||
return self._predict_input(x, node.left)
|
||||
else:
|
||||
return self._predict_input(x, node.right)
|
||||
|
||||
class DecisionTreeRegressor:
|
||||
def __init__(self, max_depth=None, min_samples_split=2, max_features=None):
|
||||
self.max_depth = max_depth
|
||||
self.min_samples_split = min_samples_split
|
||||
self.max_features = max_features
|
||||
self.tree = None
|
||||
|
||||
class Node:
|
||||
def __init__(self, feature=None, threshold=None, left=None, right=None, *, value=None):
|
||||
self.feature = feature
|
||||
self.threshold = threshold
|
||||
self.left = left
|
||||
self.right = right
|
||||
self.value = value # Leaf output value
|
||||
|
||||
def fit(self, X, y):
|
||||
X, y = np.array(X), np.array(y, dtype=float)
|
||||
self.n_features_ = X.shape[1]
|
||||
self.tree = self._build_tree(X, y, depth=0)
|
||||
return self
|
||||
|
||||
def _build_tree(self, X, y, depth):
|
||||
num_samples, _ = X.shape
|
||||
if num_samples < self.min_samples_split or (self.max_depth is not None and depth >= self.max_depth) or np.var(y) == 0:
|
||||
leaf_val = np.mean(y)
|
||||
return DecisionTreeRegressor.Node(value=leaf_val)
|
||||
best_sse = float('inf')
|
||||
best_feat, best_thr = None, None
|
||||
n_features = X.shape[1]
|
||||
features = range(n_features)
|
||||
if self.max_features is not None:
|
||||
if isinstance(self.max_features, int) and self.max_features < n_features:
|
||||
features = np.random.choice(n_features, self.max_features, replace=False)
|
||||
elif isinstance(self.max_features, float):
|
||||
k = int(n_features * self.max_features)
|
||||
features = np.random.choice(n_features, k, replace=False)
|
||||
for feat in features:
|
||||
X_col = X[:, feat]
|
||||
unique_vals = np.unique(X_col)
|
||||
if len(unique_vals) <= 1:
|
||||
continue
|
||||
thresholds = (unique_vals[:-1] + unique_vals[1:]) / 2.0
|
||||
for thr in thresholds:
|
||||
left_mask = X_col <= thr
|
||||
y_left, y_right = y[left_mask], y[~left_mask]
|
||||
if len(y_left) == 0 or len(y_right) == 0:
|
||||
continue
|
||||
# Compute sum of squared errors (SSE)
|
||||
left_mean, right_mean = np.mean(y_left), np.mean(y_right)
|
||||
sse_left = np.sum((y_left - left_mean) ** 2)
|
||||
sse_right = np.sum((y_right - right_mean) ** 2)
|
||||
sse = sse_left + sse_right
|
||||
if sse < best_sse:
|
||||
best_sse, best_feat, best_thr = sse, feat, thr
|
||||
if best_feat is None:
|
||||
leaf_val = np.mean(y)
|
||||
return DecisionTreeRegressor.Node(value=leaf_val)
|
||||
left_mask = X[:, best_feat] <= best_thr
|
||||
left_node = self._build_tree(X[left_mask], y[left_mask], depth+1)
|
||||
right_node = self._build_tree(X[~left_mask], y[~left_mask], depth+1)
|
||||
return DecisionTreeRegressor.Node(feature=best_feat, threshold=best_thr, left=left_node, right=right_node)
|
||||
|
||||
def predict(self, X):
|
||||
X = np.array(X)
|
||||
return np.array([self._predict_input(x, self.tree) for x in X])
|
||||
|
||||
def _predict_input(self, x, node):
|
||||
if node.value is not None:
|
||||
return node.value
|
||||
if x[node.feature] <= node.threshold:
|
||||
return self._predict_input(x, node.left)
|
||||
else:
|
||||
return self._predict_input(x, node.right)
|
||||
|
||||
# Random Forests (Classification and Regression)
|
||||
|
||||
"""
|
||||
Random forests train an ensemble of decision trees on bootstrapped data subsets and average their outputs . For classification, the final class is the majority vote of all trees; for regression, the output is the average prediction . Key points:
|
||||
|
||||
Bootstrap sampling: Each tree is trained on a random sample (with replacement) of the data.
|
||||
Feature randomness: When splitting, each node may consider only a random subset of features (parameter max_features).
|
||||
Aggregation: Classification uses mode of tree predictions; regression uses mean. This reduces overfitting compared to a single tree .
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
from collections import Counter
|
||||
|
||||
class RandomForestClassifier:
|
||||
def __init__(self, n_estimators=100, max_depth=None, min_samples_split=2, max_features='sqrt'):
|
||||
self.n_estimators = n_estimators
|
||||
self.max_depth = max_depth
|
||||
self.min_samples_split = min_samples_split
|
||||
self.max_features = max_features
|
||||
self.trees = []
|
||||
|
||||
def fit(self, X, y):
|
||||
X, y = np.array(X), np.array(y)
|
||||
n_samples, n_features = X.shape
|
||||
# Determine how many features to try at each split
|
||||
if self.max_features == 'sqrt':
|
||||
max_feats = int(np.sqrt(n_features))
|
||||
elif self.max_features == 'log2':
|
||||
max_feats = int(np.log2(n_features))
|
||||
elif isinstance(self.max_features, int):
|
||||
max_feats = self.max_features
|
||||
elif isinstance(self.max_features, float):
|
||||
max_feats = int(n_features * self.max_features)
|
||||
else:
|
||||
max_feats = n_features
|
||||
# Build trees
|
||||
for _ in range(self.n_estimators):
|
||||
indices = np.random.choice(n_samples, n_samples, replace=True)
|
||||
X_sample, y_sample = X[indices], y[indices]
|
||||
tree = DecisionTreeClassifier(criterion='gini',
|
||||
max_depth=self.max_depth,
|
||||
min_samples_split=self.min_samples_split,
|
||||
max_features=max_feats)
|
||||
tree.fit(X_sample, y_sample)
|
||||
self.trees.append(tree)
|
||||
return self
|
||||
|
||||
def predict(self, X):
|
||||
X = np.array(X)
|
||||
# Collect predictions from all trees
|
||||
tree_preds = np.array([tree.predict(X) for tree in self.trees]).T # shape (n_samples, n_trees)
|
||||
y_pred = []
|
||||
for preds in tree_preds:
|
||||
vote = Counter(preds).most_common(1)[0][0]
|
||||
y_pred.append(vote)
|
||||
return np.array(y_pred)
|
||||
|
||||
class RandomForestRegressor:
|
||||
def __init__(self, n_estimators=100, max_depth=None, min_samples_split=2, max_features=None):
|
||||
self.n_estimators = n_estimators
|
||||
self.max_depth = max_depth
|
||||
self.min_samples_split = min_samples_split
|
||||
self.max_features = max_features
|
||||
self.trees = []
|
||||
|
||||
def fit(self, X, y):
|
||||
X, y = np.array(X), np.array(y, dtype=float)
|
||||
n_samples, n_features = X.shape
|
||||
if self.max_features == 'sqrt':
|
||||
max_feats = int(np.sqrt(n_features))
|
||||
elif self.max_features == 'log2':
|
||||
max_feats = int(np.log2(n_features))
|
||||
elif isinstance(self.max_features, int):
|
||||
max_feats = self.max_features
|
||||
elif isinstance(self.max_features, float):
|
||||
max_feats = int(n_features * self.max_features)
|
||||
else:
|
||||
max_feats = n_features
|
||||
for _ in range(self.n_estimators):
|
||||
indices = np.random.choice(n_samples, n_samples, replace=True)
|
||||
X_sample, y_sample = X[indices], y[indices]
|
||||
tree = DecisionTreeRegressor(max_depth=self.max_depth,
|
||||
min_samples_split=self.min_samples_split,
|
||||
max_features=max_feats)
|
||||
tree.fit(X_sample, y_sample)
|
||||
self.trees.append(tree)
|
||||
return self
|
||||
|
||||
def predict(self, X):
|
||||
X = np.array(X)
|
||||
# Average predictions from all trees
|
||||
tree_preds = np.array([tree.predict(X) for tree in self.trees])
|
||||
return np.mean(tree_preds, axis=0)
|
||||
|
||||
# Gradient Boosting (Classification and Regression)
|
||||
|
||||
"""
|
||||
Gradient boosting builds an additive ensemble of trees by fitting each new tree on the residuals (errors) of the existing model, effectively performing gradient descent on a loss function . At each stage m:
|
||||
|
||||
Compute the pseudo-residuals r_{im} = -\partial L(y_i, F(x_i)) / \partial F(x_i) (the negative gradient) .
|
||||
Fit a tree h_m(x) to these residuals.
|
||||
Update the model: F_m(x) = F_{m-1}(x) + \gamma_m \, h_m(x) (with line-search multiplier \gamma_m or simply a learning rate).
|
||||
|
||||
|
||||
For binary classification, we use the logistic loss: initialize F_0 = \log(p/(1-p)) (log-odds of positive class) and repeatedly fit trees to y - \sigma(F). For multiclass, we fit one-vs-rest models (one boosting ensemble per class) and predict the class with highest score. Key concepts:
|
||||
|
||||
Additive updates: Each tree’s prediction is scaled by a learning rate and added to the ensemble output.
|
||||
Regression loss: Typically squared-error (L2) for regression, logistic (cross-entropy) for classification.
|
||||
Weak learners: Trees are often shallow (small max_depth).
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
from math import log, exp
|
||||
|
||||
class GradientBoostingRegressor:
|
||||
def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3):
|
||||
self.n_estimators = n_estimators
|
||||
self.learning_rate = learning_rate
|
||||
self.max_depth = max_depth
|
||||
self.trees = []
|
||||
self.gammas = []
|
||||
self.initial_prediction = None
|
||||
|
||||
def fit(self, X, y):
|
||||
X, y = np.array(X), np.array(y, dtype=float)
|
||||
# Initialize prediction with mean
|
||||
self.initial_prediction = np.mean(y)
|
||||
F = np.full_like(y, fill_value=self.initial_prediction, dtype=float)
|
||||
for _ in range(self.n_estimators):
|
||||
residual = y - F
|
||||
tree = DecisionTreeRegressor(max_depth=self.max_depth)
|
||||
tree.fit(X, residual)
|
||||
pred = tree.predict(X)
|
||||
# Line search for optimal multiplier gamma
|
||||
gamma = np.dot(residual, pred) / (np.dot(pred, pred) + 1e-8)
|
||||
F = F + self.learning_rate * gamma * pred
|
||||
self.trees.append(tree)
|
||||
self.gammas.append(gamma)
|
||||
return self
|
||||
|
||||
def predict(self, X):
|
||||
X = np.array(X)
|
||||
F = np.full(X.shape[0], fill_value=self.initial_prediction, dtype=float)
|
||||
for tree, gamma in zip(self.trees, self.gammas):
|
||||
F += self.learning_rate * gamma * tree.predict(X)
|
||||
return F
|
||||
|
||||
class GradientBoostingClassifier:
|
||||
def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3):
|
||||
self.n_estimators = n_estimators
|
||||
self.learning_rate = learning_rate
|
||||
self.max_depth = max_depth
|
||||
self.models = [] # For multiclass, one model per class
|
||||
|
||||
def fit(self, X, y):
|
||||
X, y = np.array(X), np.array(y)
|
||||
self.classes_ = np.unique(y)
|
||||
if len(self.classes_) <= 2:
|
||||
# Binary classification (labels may be 0/1 or not)
|
||||
# Map labels to 0/1
|
||||
if set(self.classes_) != {0, 1}:
|
||||
class0, class1 = self.classes_[0], self.classes_[1]
|
||||
y_bin = np.array([0 if yi==class0 else 1 for yi in y])
|
||||
self.class_map = {0: class0, 1: class1}
|
||||
else:
|
||||
y_bin = y
|
||||
self.class_map = None
|
||||
# Initialize log-odds
|
||||
p = np.clip(np.mean(y_bin), 1e-6, 1-1e-6)
|
||||
F = np.full(y_bin.shape, fill_value=log(p/(1-p)), dtype=float)
|
||||
self.initial_F = F[0]
|
||||
self.trees = []
|
||||
for _ in range(self.n_estimators):
|
||||
P = 1 / (1 + np.exp(-F))
|
||||
residual = y_bin - P
|
||||
tree = DecisionTreeRegressor(max_depth=self.max_depth)
|
||||
tree.fit(X, residual)
|
||||
pred = tree.predict(X)
|
||||
F = F + self.learning_rate * pred
|
||||
self.trees.append(tree)
|
||||
else:
|
||||
# Multiclass one-vs-rest
|
||||
for cls in self.classes_:
|
||||
y_binary = (y == cls).astype(int)
|
||||
model = GradientBoostingClassifier(n_estimators=self.n_estimators,
|
||||
learning_rate=self.learning_rate,
|
||||
max_depth=self.max_depth)
|
||||
model.fit(X, y_binary)
|
||||
self.models.append(model)
|
||||
return self
|
||||
|
||||
def predict(self, X):
|
||||
X = np.array(X)
|
||||
if len(self.classes_) <= 2:
|
||||
# Binary
|
||||
F = np.full(X.shape[0], fill_value=self.initial_F, dtype=float)
|
||||
for tree in self.trees:
|
||||
F += self.learning_rate * tree.predict(X)
|
||||
P = 1 / (1 + np.exp(-F))
|
||||
y_pred = (P >= 0.5).astype(int)
|
||||
if self.class_map:
|
||||
inv_map = {v:k for k,v in self.class_map.items()}
|
||||
y_pred = np.array([inv_map[val] for val in y_pred])
|
||||
return y_pred
|
||||
else:
|
||||
# Multiclass: compute score for each class
|
||||
scores = []
|
||||
for model in self.models:
|
||||
F_cls = np.full(X.shape[0], fill_value=model.initial_F, dtype=float)
|
||||
for tree in model.trees:
|
||||
F_cls += self.learning_rate * tree.predict(X)
|
||||
scores.append(F_cls)
|
||||
scores = np.vstack(scores).T # shape (n_samples, n_classes)
|
||||
class_idx = np.argmax(scores, axis=1)
|
||||
return np.array([self.classes_[i] for i in class_idx])
|
||||
|
||||
|
||||
# Classification Example: Generate a 2-class dataset, train models, and evaluate accuracy.
|
||||
import numpy as np
|
||||
import csv
|
||||
|
||||
# Synthetic binary classification data
|
||||
np.random.seed(0)
|
||||
N = 100
|
||||
# Class 0 centered at (-2, -2), Class 1 at (2, 2)
|
||||
X0 = np.random.randn(N, 2) - 2
|
||||
X1 = np.random.randn(N, 2) + 2
|
||||
X_clf = np.vstack([X0, X1])
|
||||
y_clf = np.array([0]*N + [1]*N)
|
||||
|
||||
# Shuffle data
|
||||
perm = np.random.permutation(len(y_clf))
|
||||
X_clf, y_clf = X_clf[perm], y_clf[perm]
|
||||
|
||||
# Train models
|
||||
dt_clf = DecisionTreeClassifier(max_depth=3)
|
||||
dt_clf.fit(X_clf, y_clf)
|
||||
rf_clf = RandomForestClassifier(n_estimators=10, max_depth=3)
|
||||
rf_clf.fit(X_clf, y_clf)
|
||||
gb_clf = GradientBoostingClassifier(n_estimators=20, learning_rate=0.1, max_depth=2)
|
||||
gb_clf.fit(X_clf, y_clf)
|
||||
|
||||
# Predictions
|
||||
pred_dt = dt_clf.predict(X_clf)
|
||||
pred_rf = rf_clf.predict(X_clf)
|
||||
pred_gb = gb_clf.predict(X_clf)
|
||||
|
||||
# Accuracy evaluation
|
||||
acc_dt = np.mean(pred_dt == y_clf)
|
||||
acc_rf = np.mean(pred_rf == y_clf)
|
||||
acc_gb = np.mean(pred_gb == y_clf)
|
||||
print(f"Decision Tree Accuracy: {acc_dt:.2f}")
|
||||
print(f"Random Forest Accuracy: {acc_rf:.2f}")
|
||||
print(f"Gradient Boosting Accuracy: {acc_gb:.2f}")
|
||||
|
||||
# Export to CSV
|
||||
with open('classification_results.csv', 'w', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow(['GroundTruth', 'DT_Pred', 'RF_Pred', 'GB_Pred'])
|
||||
for true, d, r, g in zip(y_clf, pred_dt, pred_rf, pred_gb):
|
||||
writer.writerow([true, d, r, g])
|
||||
|
||||
# Regression Example: Generate a simple regression dataset, train models, and compute MSE.
|
||||
import numpy as np
|
||||
import csv
|
||||
|
||||
# Synthetic regression data: y = 3*x1 - 2*x2 + noise
|
||||
np.random.seed(1)
|
||||
N = 200
|
||||
X_reg = np.random.randn(N, 2)
|
||||
y_reg = 3 * X_reg[:,0] - 2 * X_reg[:,1] + np.random.randn(N) * 0.5
|
||||
|
||||
# Train models
|
||||
dt_reg = DecisionTreeRegressor(max_depth=4)
|
||||
dt_reg.fit(X_reg, y_reg)
|
||||
rf_reg = RandomForestRegressor(n_estimators=10, max_depth=4)
|
||||
rf_reg.fit(X_reg, y_reg)
|
||||
gb_reg = GradientBoostingRegressor(n_estimators=50, learning_rate=0.1, max_depth=2)
|
||||
gb_reg.fit(X_reg, y_reg)
|
||||
|
||||
# Predictions
|
||||
pred_dt_r = dt_reg.predict(X_reg)
|
||||
pred_rf_r = rf_reg.predict(X_reg)
|
||||
pred_gb_r = gb_reg.predict(X_reg)
|
||||
|
||||
# MSE evaluation
|
||||
mse_dt = np.mean((pred_dt_r - y_reg)**2)
|
||||
mse_rf = np.mean((pred_rf_r - y_reg)**2)
|
||||
mse_gb = np.mean((pred_gb_r - y_reg)**2)
|
||||
print(f"Decision Tree MSE: {mse_dt:.3f}")
|
||||
print(f"Random Forest MSE: {mse_rf:.3f}")
|
||||
print(f"Gradient Boosting MSE: {mse_gb:.3f}")
|
||||
|
||||
# Export to CSV
|
||||
with open('regression_results.csv', 'w', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow(['GroundTruth', 'DT_Pred', 'RF_Pred', 'GB_Pred'])
|
||||
for true, d, r, g in zip(y_reg, pred_dt_r, pred_rf_r, pred_gb_r):
|
||||
writer.writerow([true, d, r, g])
|
||||
|
||||
@@ -0,0 +1,201 @@
|
||||
GroundTruth,DT_Pred,RF_Pred,GB_Pred
|
||||
5.4432818818678665,4.587413399100595,4.867399782178129,5.790888921291713
|
||||
0.5996122276017699,-0.17286001207987395,0.6392547204856381,0.3346234065402876
|
||||
7.382916188676534,4.019798593894754,4.728583892005671,6.893862359327972
|
||||
7.373298690627765,8.311136903189661,6.341214611261762,6.641424617183763
|
||||
1.2444295584307281,0.7829612037013028,1.682913433262075,1.9593407654201414
|
||||
8.549837433392373,8.311136903189661,7.361883475207449,8.003853760576028
|
||||
-1.2703762672423755,-0.17286001207987395,-0.01653138757934134,-0.254473927488898
|
||||
5.18600642962302,4.019798593894754,3.973058549440549,5.08945059431903
|
||||
1.464240188469057,1.7340615070817575,1.701365449146443,1.3513265538013948
|
||||
-0.4869020241332592,0.7829612037013028,0.3225235907484086,-0.6615231670329242
|
||||
-5.732173085850799,-4.339380045700343,-5.096052232059108,-5.773972604400032
|
||||
2.7279612455737965,0.7829612037013028,1.2126254014394149,2.2910512207060636
|
||||
4.9501481793664475,4.019798593894754,3.564288400025178,4.514052426711978
|
||||
1.4725419460747882,1.7340615070817575,1.701365449146443,1.3513265538013948
|
||||
-3.071126672941314,-3.025574053843865,-2.88365816162571,-2.1926790779131244
|
||||
-2.170258389366966,-1.5513454724715776,-1.2777089856188693,-1.1235787567005935
|
||||
-0.7600362306750654,-1.5513454724715776,-1.2777089856188693,-0.7497144820358775
|
||||
-1.43048864071057,-1.5513454724715776,-1.4360687647137091,-1.2677563323563124
|
||||
-3.665626297648134,-3.025574053843865,-3.6540151419624793,-3.841016720812165
|
||||
2.448194302063534,4.587413399100595,3.597183239144573,3.4642327450250083
|
||||
1.0853683566497465,1.7340615070817575,1.6514228531497448,1.0153227147797779
|
||||
-4.819703396026448,-4.339380045700343,-4.255139465994213,-4.718460794735865
|
||||
1.2390122138154673,1.7340615070817575,1.3496566838538135,1.2168193423286342
|
||||
-4.002748627593119,-1.7909353004230013,-1.855366215191576,-3.0079622328719537
|
||||
0.15338268928910692,0.7829612037013028,0.3144003745402276,-0.6306150644612731
|
||||
1.6317154212536937,1.7340615070817575,1.9416888999030886,1.5279158676213782
|
||||
-2.9684476986337054,-1.5513454724715776,-2.0519042321805316,-2.6752759452833175
|
||||
-1.6248455030745903,-3.025574053843865,-2.2335476362719087,-1.903918053168852
|
||||
0.6633284422034442,0.7829612037013028,0.25432718961037304,0.7461319230554601
|
||||
-1.1280914918962268,0.7829612037013028,0.3194589353167796,-0.6717758998797576
|
||||
-4.164701969180553,-4.339380045700343,-4.255139465994213,-4.697271546092074
|
||||
2.692826032851701,4.019798593894754,2.689732056504237,2.3182495894505046
|
||||
2.037128644725154,0.7829612037013028,1.4761891913388827,1.4795864630555156
|
||||
0.30381092072311466,-1.7909353004230013,-0.7092680334850268,0.31557743212992867
|
||||
9.923169078775501,10.509218150849213,9.02003515775919,9.429862099478795
|
||||
-3.3482609799752034,-3.3432405724417764,-3.1227871611703937,-3.3547859184344153
|
||||
-1.0389050002745381,0.7829612037013028,0.1136107068795125,-1.05769970141103
|
||||
5.508150707342573,4.019798593894754,4.0220233007743555,4.970341115762031
|
||||
-2.1701391429667667,-3.025574053843865,-3.0405184152400695,-2.6197637148628807
|
||||
0.06113918707223631,0.7829612037013028,0.3144003745402276,-1.05769970141103
|
||||
-0.039826281950997466,-0.17286001207987395,-0.43425679234571896,-0.35747208129069635
|
||||
-1.1024491143857782,0.7829612037013028,0.3225235907484086,-0.5069711829117487
|
||||
-0.2232031837257465,0.7829612037013028,0.6080868319430042,0.17379963398440912
|
||||
-2.092061022168067,-3.025574053843865,-3.2130724780464703,-2.6395826604071075
|
||||
-2.0591455878158427,-1.7909353004230013,-0.5807417422123871,-1.7787408330867247
|
||||
3.4197103773031787,0.7829612037013028,1.8359115230257324,2.40718144444212
|
||||
-0.2741218636273111,-0.17286001207987395,-0.426498988536295,0.1080940522042256
|
||||
1.6162436373353701,0.7829612037013028,1.2700395612239626,1.3393209055199082
|
||||
-1.311170864798116,-0.17286001207987395,-0.8575825422373018,-0.7229009684417831
|
||||
-2.5270124806840655,-3.025574053843865,-3.369932731660829,-2.9343238155264455
|
||||
-4.056518114418082,-4.339380045700343,-3.6688578553761966,-3.4965064757326965
|
||||
0.582384577257114,0.7829612037013028,0.8061891073857292,0.8434445188007198
|
||||
-3.2863023509300273,-3.025574053843865,-3.4500901420054872,-3.0274839314000364
|
||||
3.767874604197618,4.019798593894754,3.564288400025178,3.7975335659649185
|
||||
-0.3143864450239937,-0.17286001207987395,-0.8575825422373018,-0.6510988932866465
|
||||
-5.200487990062849,-6.558872933068393,-5.246777111543906,-4.881207106124984
|
||||
3.846280229966947,4.019798593894754,3.564288400025178,3.9069174951211596
|
||||
4.037060404523531,4.019798593894754,3.4377528996394346,4.361781992467218
|
||||
2.802787180875442,1.7340615070817575,1.701365449146443,2.620368262532006
|
||||
2.2495305152060814,0.7829612037013028,1.4761891913388827,2.3383447286871277
|
||||
1.304522016105788,1.7340615070817575,1.701365449146443,1.3513265538013948
|
||||
-0.6130203029693974,-1.7909353004230013,-0.730810838494616,-0.6874492035586662
|
||||
-7.523776449408949,-6.558872933068393,-7.016913930010119,-7.776608228535077
|
||||
4.511186215540193,4.587413399100595,4.402510058651032,4.01152677230024
|
||||
-5.359159080438843,-6.558872933068393,-5.762029971793566,-5.447744904373908
|
||||
0.6108843157652932,1.7340615070817575,1.0905601503569895,1.0488228857812256
|
||||
-4.566171642569727,-6.558872933068393,-5.246777111543906,-4.671529962596242
|
||||
4.508149607377162,4.019798593894754,3.564288400025178,3.445798594983944
|
||||
4.285896691732519,4.019798593894754,3.564288400025178,4.108008262663981
|
||||
2.0582199316280425,0.7829612037013028,1.4761891913388827,1.7544714629847056
|
||||
0.032492031960675516,0.7829612037013028,0.20820495778850184,0.2077976181180528
|
||||
1.046449605236043,0.7829612037013028,0.5741700408947233,1.812593909185824
|
||||
1.2134944611614933,0.7829612037013028,1.4761891913388827,1.3948339401356011
|
||||
-0.07006145268286867,0.7829612037013028,0.3144003745402276,-0.6615231670329242
|
||||
-0.8770880850318181,0.7829612037013028,0.3144003745402276,-0.8017123380310867
|
||||
3.699732750096395,2.7501162166980215,2.5912952314524196,2.696194024570413
|
||||
-2.2335381319928893,-1.7909353004230013,-1.7385878678374518,-0.9679068353003484
|
||||
0.8232392245461774,0.7829612037013028,1.2700395612239626,1.3393209055199082
|
||||
0.05433561322881994,0.7829612037013028,0.661536104096642,0.3188128978609615
|
||||
1.9488052318011708,1.7340615070817575,1.701365449146443,1.8455174069013591
|
||||
0.7004771824314627,-0.17286001207987395,0.6392547204856381,0.2426012636769862
|
||||
0.7822442627220687,0.7829612037013028,0.8379825835296415,0.8133573900520248
|
||||
1.451385378253016,0.7829612037013028,1.4761891913388827,1.690775462532898
|
||||
2.869223114270844,0.7829612037013028,1.4761891913388827,2.359887936461056
|
||||
11.095267222922924,10.509218150849213,9.02003515775919,10.549250452247348
|
||||
-3.4474874473501362,-3.025574053843865,-3.572923544234338,-3.4551243091154564
|
||||
8.681162096710741,8.429822177836392,6.104579039773336,7.762735508529412
|
||||
1.4359325502485811,0.7829612037013028,0.8379825835296415,0.5300018973383436
|
||||
5.089031641291861,4.587413399100595,4.286043383540633,4.904037248963674
|
||||
2.2205926982816435,4.019798593894754,2.689732056504237,2.3182495894505046
|
||||
-4.497444062918618,-6.558872933068393,-5.246777111543906,-4.479049365291725
|
||||
-0.7617994894160899,-1.7909353004230013,-0.7092680334850268,-0.1599876363739885
|
||||
0.6959595975286552,1.7340615070817575,1.3496566838538135,1.1598381655241343
|
||||
1.432782124042226,0.7829612037013028,1.4761891913388827,1.3948339401356011
|
||||
0.19864249616523988,0.7829612037013028,0.8429986658729556,0.45124708827155163
|
||||
2.327716287801658,0.7829612037013028,1.8359115230257324,2.3383447286871277
|
||||
3.7563026006945215,4.587413399100595,4.005070638228162,3.661558931299579
|
||||
4.113482813976909,4.019798593894754,4.088012379079276,4.88285911829463
|
||||
0.41253718245049165,0.7829612037013028,1.2126254014394149,1.3259972243806084
|
||||
0.3351116700410785,0.7829612037013028,0.25432718961037304,0.7461319230554601
|
||||
-3.7103430495773573,-3.025574053843865,-3.0405184152400695,-2.6915657900180174
|
||||
-5.568111945034292,-4.339380045700343,-4.354224575252421,-5.47155094338898
|
||||
-0.8742516241000576,-1.5513454724715776,-2.2746654204146557,-1.3955644674004062
|
||||
-3.3168499268966194,-3.3432405724417764,-3.1227871611703937,-2.818344255785449
|
||||
-7.3989954297867335,-6.558872933068393,-6.275717813009518,-7.716795880802433
|
||||
1.8004996832996478,2.7501162166980215,1.0492223214425918,1.5100929070361524
|
||||
4.234302440691409,4.019798593894754,3.564288400025178,3.835373501193173
|
||||
-5.417394781261496,-6.558872933068393,-5.762029971793566,-5.576915838757433
|
||||
2.4394214953198614,4.019798593894754,3.564288400025178,2.546013595187578
|
||||
4.242453596340928,4.587413399100595,4.402510058651032,4.01152677230024
|
||||
4.692271589216133,4.587413399100595,4.402510058651032,3.942690056545247
|
||||
4.556046118633867,4.587413399100595,4.402510058651032,4.01152677230024
|
||||
-8.473035861386107,-6.558872933068393,-7.2803130888251415,-7.982008933571979
|
||||
-1.8216507637505108,-1.5513454724715776,-1.1902386778102552,-1.0774687816744362
|
||||
-1.3477235040044544,-1.5513454724715776,-1.2777089856188693,-1.1235787567005935
|
||||
-0.05468569878074042,0.7829612037013028,0.6080868319430042,0.17379963398440912
|
||||
-2.0056791533911196,-1.7909353004230013,-1.0259029774097224,-1.1171700125187718
|
||||
2.3835674819563475,1.7340615070817575,1.701365449146443,1.9974521224608754
|
||||
-2.124925970951713,-1.5513454724715776,-2.0519042321805316,-2.2405904800263436
|
||||
-6.1190593462602365,-6.558872933068393,-6.052742811723908,-6.160005891605775
|
||||
-0.151232270039378,-0.17286001207987395,-0.426498988536295,-0.3262760026440347
|
||||
2.5177256195756064,1.7340615070817575,1.701365449146443,2.5470161220054233
|
||||
-3.8691836386260987,-3.025574053843865,-3.8269338070992065,-3.295323408672829
|
||||
3.622168845547816,4.019798593894754,3.564288400025178,2.546013595187578
|
||||
8.526328212606373,8.311136903189661,7.122440618979657,7.837114185387792
|
||||
-0.216757619042365,-0.17286001207987395,-0.426498988536295,-0.5787233927860647
|
||||
-11.601209595104056,-11.601209595104056,-9.561185675768339,-10.512002827005508
|
||||
2.88587332285357,4.019798593894754,3.294860695394168,3.1660924242631885
|
||||
5.300488032934883,4.587413399100595,5.062997672072164,4.778062512164518
|
||||
3.20720191114799,4.019798593894754,3.129889275456901,2.4642040664303697
|
||||
4.180484947375243,1.7340615070817575,2.084779047349223,2.6512763651036573
|
||||
2.730862512269863,4.019798593894754,2.975295922574877,2.5019657458514053
|
||||
-2.1278022385512543,-1.7909353004230013,-0.8260463808391512,-1.8626403871257085
|
||||
0.5206670284828185,0.7829612037013028,0.7895493937193178,0.27532572182334797
|
||||
2.1515424507522685,1.7340615070817575,2.2662416091255366,1.7676186374592735
|
||||
-0.7746121832101851,-1.7909353004230013,-0.7092680334850268,-0.1599876363739885
|
||||
0.8486000154550029,0.7829612037013028,0.3733803732340081,0.7461319230554601
|
||||
-1.0729751024422207,-1.5513454724715776,-1.6437232249048663,-1.0646041001561037
|
||||
-7.6810906601322495,-6.558872933068393,-7.016913930010119,-7.193345627322054
|
||||
-3.381303829864293,-4.339380045700343,-3.6688578553761966,-3.44589364922135
|
||||
-1.682963477688704,-1.5513454724715776,-1.3774324801021678,-1.4057173607400517
|
||||
-1.9223903666104163,0.7829612037013028,-0.005442476744122615,-1.328958564740324
|
||||
-1.022867997944866,0.7829612037013028,0.6080868319430042,0.14289153141275795
|
||||
0.20211849485077876,0.7829612037013028,0.6080868319430042,0.14289153141275795
|
||||
-4.624820571525798,-3.025574053843865,-3.1145876393796565,-3.5566190071198314
|
||||
-1.295754821123852,0.7829612037013028,-0.06451881681766505,-0.017190941160655657
|
||||
2.7004429185471466,4.019798593894754,3.564288400025178,3.0126208930108955
|
||||
0.9556774761584665,0.7829612037013028,0.8379825835296415,0.7824492874803737
|
||||
0.3372176657347352,0.7829612037013028,0.5741700408947233,0.6723472452309062
|
||||
-4.615587966790981,-3.025574053843865,-3.8269338070992065,-3.8318986298457185
|
||||
9.050847714329977,8.311136903189661,8.191023038289902,8.874580922666423
|
||||
-1.484588673800784,-1.5513454724715776,-1.5165767561053662,-1.0646041001561037
|
||||
-3.5773878046504186,-4.339380045700343,-3.6688578553761966,-3.44589364922135
|
||||
0.6911638893558922,1.7340615070817575,1.3496566838538135,1.2168193423286342
|
||||
2.993421702554793,4.019798593894754,3.564288400025178,2.8539311695381246
|
||||
3.27619429505266,0.7829612037013028,1.4761891913388827,2.40718144444212
|
||||
5.3778196611518885,4.587413399100595,5.121782776091066,5.847870098096212
|
||||
8.178482258962045,8.429822177836392,5.884152196000407,6.727542304249694
|
||||
1.631113241380004,0.7829612037013028,1.4761891913388827,1.570755306583805
|
||||
-0.25665876738266513,-1.5513454724715776,-1.2777089856188693,-0.7497144820358775
|
||||
1.613248685596358,1.7340615070817575,1.701365449146443,1.3513265538013948
|
||||
2.4303661352193653,1.7340615070817575,2.2662416091255366,1.7676186374592735
|
||||
0.6441618263907917,0.7829612037013028,0.25432718961037304,0.7461319230554601
|
||||
-4.228828865372143,-4.339380045700343,-4.104814639901052,-4.2600649948301355
|
||||
-3.843126375937169,-4.339380045700343,-3.6688578553761966,-3.4247044005775598
|
||||
1.0491437505785097,0.7829612037013028,-0.09835331046329601,0.5146975219915633
|
||||
-2.4531495909852636,-1.5513454724715776,-1.9504228619236361,-2.2903184452192407
|
||||
5.644033124047758,4.587413399100595,5.121782776091066,5.790888921291713
|
||||
-2.9258317234227125,-3.3432405724417764,-3.1227871611703937,-2.868478980566626
|
||||
-3.2501102047917234,-3.3432405724417764,-3.1227871611703937,-2.868478980566626
|
||||
-4.021945070669235,-4.339380045700343,-3.6688578553761966,-3.5176957243764866
|
||||
-1.6515657127272214,-3.025574053843865,-2.88365816162571,-2.072000555763744
|
||||
-0.16123721575693717,0.7829612037013028,0.38881232949047506,0.3188128978609615
|
||||
0.7702689119165229,0.7829612037013028,0.661536104096642,0.5300018973383436
|
||||
3.806583329076842,4.019798593894754,3.564288400025178,3.8585467170918344
|
||||
1.4470984055029208,0.7829612037013028,0.3733803732340081,1.5274697629800138
|
||||
-2.0097304453363454,-3.025574053843865,-2.2335476362719087,-2.3310026901186074
|
||||
-1.1038321325505507,-1.5513454724715776,-1.2777089856188693,-1.0908277426198256
|
||||
0.7575335023199609,0.7829612037013028,0.8379825835296415,0.5609099999099947
|
||||
0.5491820808925653,-0.17286001207987395,-0.3003167401365543,0.1080940522042256
|
||||
-3.160808086081791,-3.025574053843865,-3.572923544234338,-3.695475069873099
|
||||
-6.849679747770614,-6.558872933068393,-5.540346982806637,-5.714238735108081
|
||||
-2.721655906924753,-3.025574053843865,-2.857103436975195,-2.6197637148628807
|
||||
7.076584763750515,4.019798593894754,4.728583892005671,5.928596671480644
|
||||
1.6861113353726331,0.7829612037013028,1.4761891913388827,1.3948339401356011
|
||||
0.989171449301451,1.7340615070817575,1.3496566838538135,1.0102132932174468
|
||||
4.259119829902918,4.019798593894754,3.564288400025178,3.5112792830349058
|
||||
3.9878520254237158,4.587413399100595,4.402510058651032,4.301496507476102
|
||||
8.05537246499182,8.311136903189661,7.201878039666836,7.6788913544623965
|
||||
-7.95550804615306,-6.558872933068393,-6.052742811723908,-6.743268492818798
|
||||
2.189558050611199,0.7829612037013028,1.4761891913388827,1.9252416341411072
|
||||
-3.8751500271226234,-3.3432405724417764,-3.1227871611703937,-3.3547859184344153
|
||||
2.47777814031454,0.7829612037013028,2.0924732054503736,1.7819443060611873
|
||||
0.8582933002418842,0.7829612037013028,0.661536104096642,0.5300018973383436
|
||||
-1.7182315200304354,-1.5513454724715776,-1.6437232249048663,-1.4057173607400517
|
||||
-7.088627367379241,-6.558872933068393,-6.960305887457423,-7.033602563950763
|
||||
-2.0504984508160695,-3.025574053843865,-2.88365816162571,-2.1408372715187363
|
||||
-4.16153822302006,-3.025574053843865,-3.8269338070992065,-4.523796397004544
|
||||
-7.693790597428782,-6.558872933068393,-6.708641281236406,-6.983619253576441
|
||||
-3.634818210013233,-1.7909353004230013,-2.0378476758861166,-3.0079622328719537
|
||||
|
Reference in New Issue
Block a user