// Run applies a trained BinningFilter to a set of Instances,
// discretising any numeric attributes added.
//
// IMPORTANT: Run discretises in-place, so make sure to take
// a copy if the original instances are still needed
//
// IMPORTANT: This function panic()s if the filter has not been
// trained. Call Build() before running this function
//
// IMPORTANT: Call Build() after adding any additional attributes.
// Otherwise, the training structure will be out of date from
// the values expected and could cause a panic.
func (b *BinningFilter) Run(on *base.Instances) {
	if !b.trained {
		panic("Call Build() beforehand")
	}
	for attr := range b.Attributes {
		minVal := b.MinVals[attr]
		maxVal := b.MaxVals[attr]
		disc := 0
		// Casts to float32 to replicate a floating point precision error
		delta := float32(maxVal - minVal)
		delta /= float32(b.BinCount)
		for i := 0; i < on.Rows; i++ {
			val := on.Get(i, attr)
			if val <= minVal {
				disc = 0
			} else {
				disc = int(math.Floor(float64(float32(val-minVal) / delta)))
				if disc >= b.BinCount {
					disc = b.BinCount - 1
				}
			}
			on.Set(i, attr, float64(disc))
		}
		newAttribute := new(base.CategoricalAttribute)
		newAttribute.SetName(on.GetAttr(attr).GetName())
		for i := 0; i < b.BinCount; i++ {
			newAttribute.GetSysValFromString(fmt.Sprintf("%d", i))
		}
		on.ReplaceAttr(attr, newAttribute)
	}
}

// Run discretises the set of Instances `on'
//
// IMPORTANT: ChiMergeFilter discretises in place.
func (c *ChiMergeFilter) Run(on *base.Instances) {
	if !c._Trained {
		panic("Call Build() beforehand")
	}
	for attr := range c.Tables {
		table := c.Tables[attr]
		for i := 0; i < on.Rows; i++ {
			val := on.Get(i, attr)
			dis := 0
			for j, k := range table {
				if k.Value < val {
					dis = j
					continue
				}
				break
			}
			on.Set(i, attr, float64(dis))
		}
		newAttribute := new(base.CategoricalAttribute)
		newAttribute.SetName(on.GetAttr(attr).GetName())
		for _, k := range table {
			newAttribute.GetSysValFromString(fmt.Sprintf("%f", k.Value))
		}
		on.ReplaceAttr(attr, newAttribute)
	}
}

// GetAttributesAfterFiltering gets a list of before/after
// Attributes as base.FilteredAttributes
func (d *AbstractDiscretizeFilter) GetAttributesAfterFiltering() []base.FilteredAttribute {
	oldAttrs := d.train.AllAttributes()
	ret := make([]base.FilteredAttribute, len(oldAttrs))
	for i, a := range oldAttrs {
		if d.attrs[a] {
			retAttr := new(base.CategoricalAttribute)
			retAttr.SetName(a.GetName())
			ret[i] = base.FilteredAttribute{a, retAttr}
		} else {
			ret[i] = base.FilteredAttribute{a, a}
		}
	}
	return ret
}

// GetAttributesAfterFiltering gets a list of before/after
// Attributes as base.FilteredAttributes
func (c *ChiMergeFilter) GetAttributesAfterFiltering() []base.FilteredAttribute {
	oldAttrs := c.train.AllAttributes()
	ret := make([]base.FilteredAttribute, len(oldAttrs))
	for i, a := range oldAttrs {
		if c.attrs[a] {
			retAttr := new(base.CategoricalAttribute)
			retAttr.SetName(a.GetName())
			for _, k := range c.tables[a] {
				retAttr.GetSysValFromString(fmt.Sprintf("%f", k.Value))
			}
			ret[i] = base.FilteredAttribute{a, retAttr}
		} else {
			ret[i] = base.FilteredAttribute{a, a}
		}
	}
	return ret
}

// Fit creates n filtered datasets (where n is the number of values
// a CategoricalAttribute can take) and uses them to train the
// underlying classifiers.
func (m *OneVsAllModel) Fit(using base.FixedDataGrid) {
	var classAttr *base.CategoricalAttribute
	// Do some validation
	classAttrs := using.AllClassAttributes()
	for _, a := range classAttrs {
		if c, ok := a.(*base.CategoricalAttribute); !ok {
			panic("Unsupported ClassAttribute type")
		} else {
			classAttr = c
		}
	}
	attrs := m.generateAttributes(using)

	// Find the highest stored value
	val := uint64(0)
	classVals := classAttr.GetValues()
	for _, s := range classVals {
		cur := base.UnpackBytesToU64(classAttr.GetSysValFromString(s))
		if cur > val {
			val = cur
		}
	}
	if val == 0 {
		panic("Must have more than one class!")
	}
	m.maxClassVal = val

	// Create individual filtered instances for training
	filters := make([]*oneVsAllFilter, val+1)
	classifiers := make([]base.Classifier, val+1)
	for i := uint64(0); i <= val; i++ {
		f := &oneVsAllFilter{
			attrs,
			classAttr,
			i,
		}
		filters[i] = f
		classifiers[i] = m.NewClassifierFunction(classVals[int(i)])
		classifiers[i].Fit(base.NewLazilyFilteredInstances(using, f))
	}

	m.filters = filters
	m.classifiers = classifiers
}

// GetAttributesAfterFiltering gets a list of before/after
// Attributes as base.FilteredAttributes
func (b *BinningFilter) GetAttributesAfterFiltering() []base.FilteredAttribute {
	oldAttrs := b.train.AllAttributes()
	ret := make([]base.FilteredAttribute, len(oldAttrs))
	for i, a := range oldAttrs {
		if b.attrs[a] {
			retAttr := new(base.CategoricalAttribute)
			minVal := b.minVals[a]
			maxVal := b.maxVals[a]
			delta := float64(maxVal-minVal) / float64(b.bins)
			retAttr.SetName(a.GetName())
			for i := 0; i <= b.bins; i++ {
				floatVal := float64(i)*delta + minVal
				fmtStr := fmt.Sprintf("%%.%df", a.(*base.FloatAttribute).Precision)
				binVal := fmt.Sprintf(fmtStr, floatVal)
				retAttr.GetSysValFromString(binVal)
			}
			ret[i] = base.FilteredAttribute{a, retAttr}
		} else {
			ret[i] = base.FilteredAttribute{a, a}
		}
	}
	return ret
}