Machine Learning dataset for Automated Constructive Trigonometry and Geometrically Trigonometry Theorem Prover
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Dear Autocad Experts,
Thanking first to Autocad for the best options you have given to us to think Geometrically. I learnt Geometry visualization through Autocad and learnt my first programming with vba lisp and c sharp through Autocad only
I need help from you to prepare a Machine Learning model such that we can automatically check which 2D Line segments are trigonometrically related or which of these line segments will always intersect or which of these line segments are on same circle etc...
I am giving the sample dataset here which has columns as below mentioned. (Geometrifying Trigonometry is Geometrization of Trigonometry is Geometrizing Trigonometry where we try to solve and inquire every Trigonometry expressions purely with Euclidean Geometry) If we can Generate all geometries of all kinds of Trigonometry Expressions then we can have lots of Deep Solutions insights with us
Interestingly AutoCAD's command Align and Scale to fit is gluing as multiplication. Division is also Aligning and scaled to fit. When several line segments are grouped due to aligning and scaled to fit then these bunches are GTSIMPLEX objects
When several such GTSIMPLEX objects are added(no multiplication only addition) then it forms Locked Sets
All of these things are like parametric blocks. With this kind of conventions we can automatically construct all geometries for all kinds of Trigonometry expressions if we can get grasp on the machine learning systems used on these kind of data sets.
Trigonometry means relating several line segments through algebra like expressions and all these Algebra like expressions hides geometry inside these parsing possibilities. Dynamic programming tool is attached such that we can prepare more numbers of machine learning datasets for more detailed predictive models.
Please help me to build the machine learning model.
I am new to Machine Learning but in one problem solving case i have used AutoCAD's commands to identify properties of Geometry interpretations of Trigonometry expressions.
If we assume Triangles have 3 points (Pivot point where SEEDS ANGLE is written , one is 90 degrees which is stretch point and the complementary angle is formed at Nodal point)
We can construct Triangles in 8 different orientations but every orientations converge to anti clock or clock wise line segments flow(As vectors are drawn)
These are the columns in the dataset.
Please feel free to ask me whenever any doubt is there. I am learning new AI systems but my works are all related to CAD systems structural engineering , Automated Geometric Junctions Theory , Automating construction tools but i am trying to learn these new technologies of machine learning.
Please help
Purpose is to get these
Machine Learning,Geometric Data mining, interrelated line segments, Trigonometry Expression to Euclidean
Geometry
1
char_counter
2
expression_classifier_with_degrees
3
expression_classifier_without_degrees
4
output_signed_gradient
5
output_positive_gradient
6
output_positive_y_intercept_dist
7
current_command_char
8
current_orientations_char
9
current_seeds_angles_degrees
10
current_given_segments_name_coming_from_previous_states_output_consumed_differently
11
current_output_segments_name
12
current_complement_segments_name
13
given_segments_x1
14
given_segments_y1
15
given_segments_x2
16
given_segments_y2
17
output_segments_x1
18
output_segments_y1
19
output_segments_x2
20
output_segments_y2
21
complement_segments_x1
22
complement_segments_y1
23
complement_segments_x2
24
complement_segments_y2
25
zoom_to_fit_frames_min_x
26
zoom_to_fit_frames_min_y
27
zoom_to_fit_frames_max_x
28
zoom_to_fit_frames_max_y
29
zoom_to_fit_frames_width(energy_effort)
30
zoom_to_fit_frames_height(energy_effort)
31
zoom_to_fit_frames_area(energy_effort)
32
pivot_x
33
pivot_y
34
pivot_z
35
stretch_x
36
stretch_y
37
stretch_z
38
nodal_x
39
nodal_y
40
nodal_z
41
current_triangles_cg_x
42
current_triangles_cg_y
43
current_triangles_cg_z
44
given_recursion_sequential_inputs_segments_gt_string_address
45
output_segments_gt_string_address
46
complement_line_segments_gt_address_string
47
output_becomes_input_input_becomes_output___reverse_construction_string_command
48
0_0_to_current_base_lines_nearest_dist
49
0_0_to_current_perpendicular_lines_nearest_dist
50
0_0_to_current_hypotenuse_lines_nearest_dist
51
current_triangle_rotates_about_its_own_cg_degrees(local_moment_energy_effort)
52
current_triangle_base_line_segment_length
53
current_triangle_perpendicular_line_segment_length
54
current_triangle_hypotenuse_line_segment_length
55
pbox_width
56
pbox_height
57
width_compressed_to_fit
58
height_compressed_to_fit
59
total_command_string
60
total_orientation_string
61
cumulative_recursive_current_aabb_min_x
62
cumulative_recursive_current_aabb_min_y
63
cumulative_recursive_current_aabb_max_x
64
cumulative_recursive_current_aabb_max_y
65
cumulative_recursive_current_aabb_frames_area
66
cumulative_recursive_current_aabb_frames_perimeter
67
cumulative_recursive_current_aabb_frames_width
68
cumulative_recursive_current_aabb_frames_height
69
cumulative_recursive_current_aabb_frames_center_x
70
cumulative_recursive_aabb_current_frames_center_y
71
cumulative_recursive_current_output_triangles_cg_x
72
cumulative_recursive_current_output_triangles_cg_y
73
cumulative_recursive_current_output_triangles_cg_z
74
cos_power
75
sin_power
76
tan_power
77
sec_power
78
cosec_power
79
cot_power
80
hypotenuse_power
81
base_power
82
perpendicular_power
83
costruction_reversed_cos_power
84
costruction_reversed_sin_power
85
costruction_reversed_tan_power
86
costruction_reversed_sec_power
87
costruction_reversed_cosec_power
88
costruction_reversed_cot_power
89
costruction_reversed_hypotenuse_power
90
costruction_reversed_base_power
91
costruction_reversed_perpendicular_power
BIM Manager And Digital Lead (Structures Online)
BOOST, AR , VR ,EPM,IFC API,PDF API , CAD API ,Revit API , Advance Steel API
Founder of Geometrifying Trigonometry(C)